computational linguistics phd programs rankings

Computational Linguistics

The computational linguistics program at Stanford is one of the oldest in the country, and offers a wide range of courses and research opportunities.

We take a very broad view of computational linguistics, from theoretical investigations to practical natural language processing applications, ranging across linguistic areas like computational semantics and pragmatics, discourse and dialogue, sociolinguistics, historical linguistics, syntax and morphology, phonology, psycholinguistics, and phonetics and speech, and applications including machine translation, question answering, and sentiment analysis.

Uniting this wide variety of research is the shared ambitious goal of dealing with the complexity and the uncertainty of human language by integrating rich models of linguistic structure with sophisticated modern neural and statistical techniques.

Together with the  Computer Science Department , our department houses a wide variety of research labs, reading groups, and informal workshops on computational linguistics, and we also maintain close ties with industrial natural language processing work in Silicon Valley.  For more information, see the  Stanford Natural Language Processing Group  and the  CSLI Pragmatics Lab .

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Ph.D. in Linguistics

Ph.D. in Linguistics (General Linguistics Track) 

Note that the required courses and language requirement differ between the curriculum instituted Sept. 2018 and the prior curriculum. All other requirements are the same.

1A. Required courses (30 credits): Curriculum instituted Sept. 2018 

One graduate-level course in each of the following sub-disciplines:

• Syntax: LING 507 “Syntactic Theory I”
• Sociolinguistics: LING 532 “Sociolinguistics I”
• Language Processing and Development: LING 541 or 542 (“Language Processing and development I or II”)
• Phonology LING 552 (“Phonology II”)
• Phonetics LING 550 (“Introduction to Linguistic Phonetics”)
• Semantics LING 479 or 579 ("Semantic Theory I or II")

1B. Required courses (35 credits): Prior to Sept. 2018 

• LING 507 ("Syntax I")
• LING 508 ("Syntax II")
• LING 532 ("Sociolinguistics I")
• LING 551 ("Phonology I")
• LING 552 ("Phonology II")
• LING 550 or 553 ("Phonetics I or II")
• LING 578 or 579 ("Semantics I or II")

If a student has taken an equivalent course elsewhere, the requirement to take this course can be waived. The waiver needs to approved by the faculty in the relevant area and the GPC. Such waivers do not change the total number of credits required by the Graduate School for graduation.

2. Credits of study:

Additional courses for a minimum of 90 credits (27 of which are LING 800) to be determined by specialization and consultation with the advisory committee.

3A. Language knowledge requirement: Curriculum instituted Sept. 2018

General Linguistics Track students must satisfy one natural language requirement for the PhD. The choice of the language needs to be approved by the student’s advisory committee. The language requirement may be satisfied in one of the following three ways:

• One year of study at the university or community college level. Students who are language instructors in other UW departments can use their language teaching experience to satisfy one language requirement.
• A major research project that involves significant primary data collection that includes substantial structural analysis and results in a major paper such as a generals paper.

3B. Language knowledge requirement: Prior to   Sept. 2018

General Linguistics Track students must satisfy two natural language requirements for the PhD. Those may be satisfied in the following ways:

• Translation exam to demonstrate the ability to read linguistic literature in a foreign language; only one of the two language requirements for the PhD can be satisfied through the translation exam.

4. Colloquium conference talks:

Two papers delivered at a colloquium or conference.

5. Constitution of PhD committee:

By the end of the second year of study.

6. Generals Papers:

Two generals papers in different areas (normally 10cr LING 600). What counts as a different area is determined and needs to be approved by the student's committee.

7. General Examination:

An oral examination, in which the candidate is questioned on the two papers. The oral examination may not be scheduled until the committee has read the two papers and approved them as passing.

8. Dissertation Prospectus:

Within 6 months of the oral examination, the student will present a formal dissertation proposal to the subset of PhD committee members who constitute the reading committee, along with a proposed calendar for completion of the dissertation.

9. Final Exam:

A Final Exam on the dissertation attended by the candidate’s Supervisory Committee and open to others interested.

10. Dissertation:

A dissertation suitable for publication.

11. ABD (all but dissertation) requirement:

All degree requirements except for the dissertation and the two colloquia must be completed before the General Exam.

Ph.D. in Linguistics (Computational Linguistics Track)

The requirements for students on the computational linguistics track will meet all the same requirements as students in other specializations except :

1. Required courses:

• 2 syntax courses from among: LING 566, 507, 508
• 2 phonetics/phonology courses from among: LING 550, 551, 552, 553
• 1 semantics course from among: 578, 579
• 1 sociolinguistics course from among: LING 532, 533
• 3 Computational Linguistics courses from among: 567, 570, 571, 572, 573

3. Language knowledge requirement:

Students in Computational Linguistics must fulfill only one language requirement, but may not use a translation exam to do so. The language must be typologically substantially distinct from the student's native language; for example, a native English-speaking student would need to select a non-Indo-European language. Please refer to Language Requirements for details.

6. Generals papers:

Same as for the General Linguistics program except a Master’s thesis completed as part of the CLMS program may count as one of the two generals papers.

How to make the CLMS to PhD transition

M.A. in Linguistics

The M.A. is not required as a prerequisite to Ph.D. study.  Students enrolled in the PhD program may get an MA degree when they pass the general exam and file a request for an MA degree with the graduate school.  Students who have taken all the required courses for the PhD CompLing track may analogously file a request for an MS degree with the Graduate School, under either model A or B below. Students who would like to get an MS degree have to get their advisor's approval before filing an official request with the Graduate School.

A. Non-thesis model:

The Generals papers and Exam constitute the capstone project necessary for a master’s degree (or the student may complete the thesis model below).

B. Thesis model: 

• Required courses: Same as the required courses in PhD General Linguistics Track. 
• Language requirement: Same as the language requirement in PhD General Linguistics Track. 
• Thesis:  A thesis, written under the supervision of a Linguistics faculty member, and accepted by a second faculty reader. Normally the work is completed in 10 credits of LING 700.

Remarks on Graduate School Requirements

Students are advised to become familiar with Graduate School requirements, as well as those described on this website. If there are any questions, the student should contact the Graduate School, the Graduate Program Coordinator or the chair of the Supervisory Committee. Once admitted to the program, students should make it a regular practice to see the Graduate Program Coordinator about their progress at least once a year. All graduate students must be either registered or officially on leave. Failure to register or go on leave is interpreted as resignation from the Graduate School. Information on the Graduate School is available at http://www.grad.washington.edu . If you have any further questions or comments please contact us at [email protected]

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• Computational Linguistics

Computational linguistics is a field at the intersection of linguistics and computer science concerned with applying methods from the fields of artificial intelligence and machine learning to problems involving language

Computational linguistics is exceptionally well represented at Penn, both at the Department of Linguistics and at the Department of Computer and Information Science . Weekly meetings, such as "Clunch" (computational linguistics and lunch) and XTAG , for ongoing work in tree adjoining grammar, as well as the Institute for Research in Cognitive Science , provide students and faculty the opportunity to work together and exchange ideas on current research topics. Penn also benefits from its closeness to the Linguistic Data Consortium .

Faculty in computational linguistics often hold joint positions in Linguistics and Computer and Information Science.

Mitch Marcus developed the first computationally tractable parser that reflects the findings of syntactic theory. He also participated in creating the first hand-parsed corpus, the English Penn Treebank , which had a significant impact on the field of computational linguistics. The project has continued ever since, branching out to include a number of other languages (such as Chinese) within the past decade; the Treebank corpora have been used to train automatic taggers and parsers as well as in linguistic research.

Fernando Pereira (Research Director at Google, formerly Penn CIS Professor), whose earlier work highlighted the connections between parsing and deduction, is now a leading figure in the field of machine learning. Those colleagues have devised and teach a full program of courses in computational linguistics which are attended by students from both linguistics and computer science. Robin Clark , Anthony Kroch , Mark Liberman , and other colleagues also teach relevant courses, and the programs in linguistics and computer science have trained large numbers of graduate students with substantial expertise in both areas.

In addition to a secondary appointment in the Computer Science department, Mark Liberman is director of the Linguistic Data Consortium . The LDC constructs online corpora of diverse types in many languages, maintains a digital archive of research papers in computational linguistics, and hosts a variety of seminars and conferences. Liberman has published extensively on the theoretical and practical underpinnings of the LDC's work, especially on the construction of corpora and of formal frameworks for linguistic annotation.

Charles Yang is interested in computational models of language acquisition and language change. Specifically, he studies the interaction between the representation of linguistic information and the mechanisms of language processing and learning, with strong commitment to the empirical findings in the psychology of language.

Phonetics, prosody, natural language processing, speech communication

Natural language processing, corpus-based and statistical models for NLP

Language acquisition, language change, computational linguistics, morphology, psycholinguistics

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PhD in Computation, Cognition and Language

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Linguistics at Cambridge is unique in the UK in that study and research in theoretical and applied linguistics are integrated within in a single academic unit. We provide great variety and flexibility in course contents as well as subject-specific training and diversity of intellectual interactions.

The PhD in Computation, Cognition and Language is a PhD track for students who conduct basic and applied research in the computational study of language, communication, and cognition, in humans and machines. This research is interdisciplinary in nature and draws on methodology and insights from a range of disciplines that are now critical for the further development of language sciences, including (but not limited to) Linguistics, Cognitive Science, Computer Science, Engineering, Psychology and Neuroscience. A variety of PhD topics that fall within this remit are accepted. Our current primary areas of research are:

• natural language processing

• computational and corpus linguistics

• computational models of human language acquisition and processing

• information extraction, mining, and presentation

• multilingual technology

• educational and assistive technology

• text data technology for health

• computational digital humanities

• computational approaches to the analysis of speech

• digital forensic speech analysis

In British universities, the PhD is traditionally awarded solely on the basis of a thesis, a substantial piece of writing which reports original research into a closely defined area of enquiry. The completion of the PhD thesis is generally expected to take three to four years, and most funding is based on this assumption.  It is also possible to take a part-time route, and the expected timeframe would be five to seven years.

While the PhD is not a taught course, students will benefit from the availability of courses and seminars offered both within the MMLL Faculty and by other departments concerned with language science in Cambridge (e.g. Computer Science and Technology, Education, Engineering, Psychology, MRC Cognition and Brain Sciences Unit). All research students also benefit from a programme of professional training run at various levels within the University and enabling cross-disciplinary interactions. The programme includes seminars and workshops on e.g. giving conference papers, publishing, applications and interviews, teaching skills, and specialist linguistic training. If you wish, you are likely to be given the opportunity of gaining experience in small group teaching for colleges. There may also be opportunities to gain some experience in teaching in the Faculty.

Learning Outcomes

By the end of the programme, candidates will have acquired excellent skills, experience and knowledge to undertake postdoctoral work (research and teaching) or another related profession.

For Cambridge students applying to continue from the MPhil by Advanced Study to PhD, the minimum academic requirement is an overall distinction in the MPhil.

For Cambridge students applying to continue from the MPhil by Thesis to PhD, the usual academic requirement is a pass in the MPhil.

All applications are judged on their own merits and students must demonstrate their suitability to undertake doctoral level research.

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Departments

This course is advertised in the following departments:

• Faculty of Modern and Medieval Languages and Linguistics
• Department of Theoretical and Applied Linguistics

Key Information

3-4 years full-time, 4-7 years part-time, study mode : research, doctor of philosophy, department of theoretical and applied linguistics this course is advertised in multiple departments. please see the overview tab for more details., course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, lent 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Michaelmas 2024 (Closed)

Funding deadlines.

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

Similar Courses

• Computer Science PhD
• Linguistics: Theoretical and Applied Linguistics PhD
• Machine Learning and Machine Intelligence MPhil
• Advanced Computer Science MPhil

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Ph.D. Programs

The Department of Linguistics offers four concentrations leading to the Doctor of Philosophy (Ph.D.) degree in Linguistics (see list below). No matter the concentration, our faculty work closely with students, guiding their research and supporting their passions.

• Applied Linguistics
• Computational Linguistics
• Sociolinguistics
• Theoretical Linguistics

Applicants to the Ph.D. program are encouraged to identify prospective research advisors, at least one of whom should be in the concentration to which they apply.

After entering the program, Ph.D. students may elect to add a minor in a second one of these concentrations [new policy effective Spring 2023].

An interdisciplinary (second) concentration in Cognitive Science is also available to Ph.D. students.

Master’s in Passing

If, in their course of the Ph.D. program, a doctoral student meets all of the requirements of a M.S. degree in Linguistics, he or she may apply to receive a “Master’s in Passing.” Please consult section IV.D.3 of the Graduate School Bulletin for full details about the “in passing” or “terminal” Master’s degree.

Best Master’s in Computational Linguistics

Are you ready to discover your college program?

The Association for Computational Linguistics describes computational linguistics as the scientific study of language from a computational perspective.

Computational Linguistics (CL) combines resources from linguistics and computer science to discover how human language works. Computational linguists create tools for critical tasks such as machine translation, speech recognition, speech synthesis, grammar checking, and text mining.

Typically, computer science (CS) departments at colleges and universities offer computational linguistics as a specialization, though some linguistics departments also offer it. Some CS departments don’t offer CL as a formal specialization, but qualified students can often work with faculty to create their own focus area.

Computational linguistics graduate students take computer programming , math, and statistics courses. They examine subjects such as semantics, computational semantics, natural language processing, models in cognitive science, and phonology.

Featured Online Schools

Best online computational linguistics graduate programs, massachusetts institute of technology.

• In-State $55,510
• Out-of-state $55,510
• Retention Rate 99%
• Acceptance Rate 4%
• Students Enrolled 11,858
• Institution Type Private
• Percent Online Enrollment 1%
• Accreditation Yes

Stanford University

• Campus + Online
• In-State $55,473
• Out-of-state $55,473
• Retention Rate 98%
• Students Enrolled 17,680
• Percent Online Enrollment 5%

Harvard University

• In-State $51,143
• Out-of-state $51,143
• Acceptance Rate 3%
• Students Enrolled 21,209
• Percent Online Enrollment 38%

Carnegie Mellon University

• In-State $57,560
• Out-of-state $57,560
• Retention Rate 97%
• Acceptance Rate 11%
• Students Enrolled 16,663
• Percent Online Enrollment 31%

University of California-Berkeley

• In-State $11,928
• Out-of-state $42,954
• Retention Rate 96%
• Students Enrolled 45,745
• Institution Type Public
• Percent Online Enrollment 69%

Princeton University

• In-State $56,010
• Out-of-state $56,010
• Acceptance Rate 6%
• Students Enrolled 8,842
• Percent Online Enrollment 81%

University of Michigan-Ann Arbor

• In-State $16,404
• Out-of-state $55,326
• Acceptance Rate 18%
• Students Enrolled 51,225
• Percent Online Enrollment 34%

Columbia University

• In-State $59,450
• Out-of-state $59,450
• Students Enrolled 29,661
• Percent Online Enrollment 23%

Cornell University

• In-State $60,286
• Out-of-state $60,286
• Acceptance Rate 7%
• Students Enrolled 25,898
• Percent Online Enrollment 12%

Yale University

• In-State $59,950
• Out-of-state $59,950
• Acceptance Rate 5%
• Students Enrolled 14,806

Online Computational Linguistics Graduate Programs Ranking Guidelines

We ranked the best master’s in computer science programs based on acceptance and graduation rates, median ACT/SAT scores for accepted students, and average earnings of graduates, according to the National Center for Education Statistics .

To determine a school’s influence, reputation, and faculty strength, we used data from AcademicInfluence.com , which tracks the scholarly publications and citations of professors, graduate students, and alumni of the ranked universities.

Natural Language Processing

Both computational linguistics and natural language processing (NLP) apply formal training in linguistics, computer sciences, and machine learning. NLP allows computers to understand, analyze, and derive meaning from human language in an intelligent and useful way. NLP professionals organize and structure knowledge to perform tasks such as translation, text segmentation, and speech recognition.

NLP systems, with their ability to analyze language for meaning, perform tasks such as correcting grammar, automatically translating languages, and converting speech to text. NLP allows machines to communicate with people on conventional language-based terms, which makes it an important factor in cognitive computing.

Data scientists use NLP for log analysis of security models, risk management and regulatory compliance, and demand forecasting. Companies use NLP to improve the accuracy of documentation, enhance the efficiency of documentation processes, and identify the most pertinent information in large databases.

Related Articles

How to get into computational linguistics.

Most computational linguistics jobs require at least a master’s degree. To pursue a master’s degree, candidates must first hold a bachelor’s degree . While several undergraduate majors prepare students for graduate study in computational linguistics, a bachelor’s in linguistics or computer science provides ideal training for a master’s program.

Certain undergraduate courses may help students gain admission to a master’s program, such as courses in morphology, semantics, and statistics. Undergraduate classes in artificial intelligence and machine learning also prepare graduates for master’s programs in computational linguistics.

Many computational linguistics master’s programs set prerequisite requirements for admission. These requirements may include programming knowledge, such as the ability to program in specific languages, like Python, C++, or Java. Many programs also require or prefer applicants to have taken an undergraduate linguistics course, statistics or probability class, and foreign language courses.

Most graduate programs require a statement of purpose, letters of recommendation, and transcripts. They may also ask applicants to complete a skills assessment or provide samples of their academic work.

Computational Linguistics Careers

Computational linguistics is the most commercially viable branch of linguistics. Computational linguists can work for high-tech companies, creating and testing models for improving or developing new software in areas such as speech recognition, grammar checkers, and dictionary development. They can also work in computer-mediated language learning and artificial intelligence or in research groups at universities and government research labs.

Examples of Companies That Employ Computational Linguists

• Expert System
• SRI STAR laboratory
• Vantage Linguistics

Computational Linguistics Jobs and Salaries

Below are some common careers and average salaries for graduates with a master’s degree in computational linguistics.

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We have 4 computational linguistics PhD Projects, Programmes & Scholarships

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computational linguistics PhD Projects, Programmes & Scholarships

Digital humanities phd programme, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Arts Research Programme

Arts Research Programmes present a range of research opportunities, shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

Natural Language Generation in the Era of Large Language Models

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Cognitive Science PhD

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

PhD Studentship Opportunities in the overall Field of Cognition Research

Germany phd programme.

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

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Search Rochester.edu

• Graduate Program
• MS in Computational Linguistics

Master of Science in Computational Linguistics

The computational linguistics master's program at Rochester trains students to be conversant both in language analysis and computational techniques applied to natural language. The curriculum consists of courses in linguistics and computer science for a total of 32 credit hours.

Graduates from the computational linguistics program will be prepared for both further training at the PhD level in computer science and linguistics, as well as industry positions. A number companies such as Google, Amazon, Nuance, LexisNexis, and Oracle are searching for employees with advanced degrees in computational linguistics for positions ranging from speech recognition technology to improving translation systems to developing better models of language understanding.

The curriculum consists of courses in linguistics and computer science, in roughly a 50/50 mix, for a total of 32 credit hours. Four courses (16 credits) are required in linguistics and four courses (16 credits) in computer science. The degree also requires a culminating special written project on a topic relevant to the student's interest and in consultation with individual advisors.

This program’s coursework can typically be completed in three full-time semesters. A fourth semester is for students to prepare their program’s final assignment, project, or thesis.

Linguistics Courses

Prerequisite.

Students are required to have completed the following prerequisite course, or its equivalent.

• LING 110: Introduction to Linguistic Analysis

Track Courses

Within linguistics, students will work with an advisor to create a “track” for their coursework in one of three areas:

• Sound structure (LING 410, 427, 510)
• Grammatical structure (LING 420, 460, 461, 462, 520)
• Meaning (LING 425, 465, 466, 468, 525, 535)

Students will be encouraged to take LING 450 and LING 501 as it suits their programs.

At least one of the following:

• LING 410: Introduction to Language Sound Systems
• LING 420: Introduction to Grammatical Systems
• LING 425: Introduction to Semantic Analysis

Plus at least two from the following:

• LING 427: Topics in Phonetics and Phonology
• LING 450: Data Science for Linguistics
• LING 460: Syntactic Theory
• LING 461: Phrase Structure Grammar
• LING 462: Topics in Experimental Syntax
• LING 465: Formal Semantics
• LING 466: Pragmatics
• LING 468: Computational Semantics
• LING 481: Statistical Methods in Computational Linguistics
• LING 482: Deep Learning Methods in Computational Linguistics
• LING 501: Linguistics Graduate Proseminar
• LING 520: Syntax
• LING 525: Graduate Semantics
• LING 527: Topics in Phonetics and Phonology
• LING 535: Formal Pragmatics

Computer Science Courses

Prerequisites.

• CSC 171: The Science of Programming
• CSC 172: The Science of Data Structures
• CSC 173: Computation and Formal Systems
• MATH 150: Discrete Math
• MATH 165: Linear Algebra with Differential Equations 
• LING 424: Introduction to Computational Linguistics
• CSC 447: Natural Language Processing
• CSC 448: Statistical Speech and Language Processing

Plus at least two of the following:

• CSC 440: Data Mining
• CSC 442: Artificial Intelligence
• CSC 444: Logical Foundations of Artificial Intelligence
• CSC 446: Machine Learning

Program Faculty

Linguistics:

• Ash Asudeh , Professor and Director of the Center for Language Science
• Scott Grimm , Department Chair and Associate Professor
• Aaron White , Associate Professor and Director of Graduate Studies

Computer science:

• James Allen
• Ehsan Hoque
• Len Schubert

The future of computational linguistics

Our guest, Christopher Manning , is a computational linguist. He builds computer models that understand and generate language using math.

Words are the key component of human intelligence, he says, and why generative AI, like ChatGPT, has caused such a stir. At one time a language model could hardly produce one coherent sentence, and suddenly ChatGPT is composing five-paragraph stories and doing mathematical proofs in rhyming verse, Manning tells host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast.

Listen on your favorite podcast platform:

Related:   Christopher Manning , professor of machine learning, linguistics, and computer science

Transcripts

Russ Altman ( 00:03 ): This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you enjoy The Future of Everything, please follow or subscribe on your favorite listening app, you'll hear about new episodes and it'll help us grow. Today, Professor Christopher Manning, Chris Manning, will tell us how the intersection of linguistics and computer science has led to the remarkable progress in intelligent agents such as ChatGPT. It's the future of computational linguistics. Before we jump into this episode, a reminder and a plea, please rate and review the podcast. It'll help us improve and it'll spread the word, and then you'll know that the future of everything will never surprise you.

( 00:48 ): When I think about linguistics, I think about the study of old languages and how they are related and how they teach us about culture of humanity. I don't always think about computer science and its role in these old languages. However, recently we have seen the rise of these intelligent chatbots like ChatGPT-4, which shows remarkable capabilities in understanding human language and generating responses to a variety of questions across all areas of human endeavor. Are these chatbots a surprise or were the computational linguistics experts who've been studying this field for decades totally expecting us to achieve this capability?

( 01:33 ): Well, Professor Chris Manning is a professor of linguistics and computer science at Stanford University. He creates computational methods for studying linguistics and computational methods for having computers and humans interact. He will tell us that these capabilities were shocking even to the experts. Yes, there was some early work that anticipated progress, but what we've seen in the last year is something that nobody expected.

( 02:00 ): So Chris, you're a professor of both linguistics and computer science. Now, some people might be surprised to know that that's even a thing because we think of linguistics as the study of languages, old languages, new languages, emerging languages, and we think of computer science as a very different study, but obviously they're not that different. So could you start out just telling us what is the intersection of linguistics and computer science and why do we care about it?

Christopher Manning ( 02:27 ): Well, yeah, so linguistics is a very diverse field. I mean, a lot of the time people think first of philology and reconstructing ancient tongues, but lots of other things go on. So there are sociolinguists who look how different communities speak and use language in quite different ways, for example. But for me, what I do is deal with how we can get computers to understand, generate, learn languages, which connects into cognitive science questions because that's also what variously psychologists would study from a more sort of human-centered cognitive perspective is how does this go about, where I'm on a slightly more technological level of wanting to get our computers so that they can understand us in the same way that other human beings do. So a lot of that has a more machine learning engineering flavor of how to build models, but it also centrally depends on the subject domain. So just as for your own work, Russ, if you're working in bioinformatics, it's useful to know something about biology as well as something about computation. Similarly, there's value in understanding what the structure of human languages is.

Russ Altman ( 03:45 ): Yes, and I'm sure we're going to talk about artificial intelligence and some of the amazing things that we're starting to see in a moment. But before that, I'd like to explore a little bit the relationship of language to intelligence. Do you think about it as there's an intelligent being and that intelligent being creates language to communicate, or is it much more rich connection between how we think about things and the words we have even to think about? So talk a little bit about the relationship between language and intelligence.

Christopher Manning ( 04:14 ): Sure. I should preface it by saying this is certainly an area that's been debated a lot by philosophers and cognitive scientists, and not everyone has exactly the same view, but my personal view is that language is extremely important to human intelligence. That if you compare humans with some of our nearest neighbors, chimpanzees, bonobos and things like that, it's kind of hard to differentiate us and some of the basics of intelligence it seems, that you can look at things like planning, tool, use, memory, a lot of the things that people talk about for intelligence and to first approximation, it doesn't seem like there's much difference. I mean, in some areas chimpanzees have better short-term memory than human beings do, in fact. But nevertheless, then it just seems like there's this night and day difference between chimpanzee's intelligence and human beings intelligence and what we've been able to do with that, right?

( 05:23 ): There's a difference between having a stick to dig out some ants versus having a cell phone in your pocket, seems kind of different. And my belief is that the development of human language has been essential to leveling up human intelligence, that one side of human language is communication, and we can get back to that, but the other side is that human language gave this transformative tool for humans to think with. So it's not that can't think without language. You can think with images as people sometimes do in their dreams, and obviously you have immediate gut responses when you see something that aren't anything to do with language, you just look at it and you feel excited or repulsed, that's thinking without language. But we humans do a huge amount of structured thought, planning, consideration of alternatives in our head in a linguistic sense. I mean, all of us play out scenarios using words and thoughts in our head, and I think that has been essential in structuring and advancing human thought to allow the kind of higher level intelligence and the results of that that we see everywhere around us.

Russ Altman ( 06:44 ): Great, so you've set up my next question so perfectly. It's almost like you knew what was coming. So the elephant in the room, I think it's fair to say, is the recent release and discussions about ChatGPT and other so-called large language models or foundational models, and you've been working in this field for decades. And so, one of the things I wanted to ask you is, did ChatGPT for many people came as a shocking surprise, but I'm wondering from somebody who's been in the field, is this really a surprise or have we been making slow and steady progress for this over the last two decades and it's just the entirely predictable result of the research that you and others were doing?

Christopher Manning ( 07:26 ): It was a shocking surprise.

Russ Altman ( 07:28 ): Oh my goodness.

Christopher Manning ( 07:30 ): Yeah. So I mean, there are no doubt that you can paint out a history of the progress of research and that there were different steps along the way. And you can look back and say, well, people started using language models, these are models that sort of predict next words in the sequence around the sort of mid-seventies, and they started to show that they were useful for speech recognition and spelling correction, machine translation, and it was about 2000 that people started to use neural language models and that they showed some advantages. And then for some of the kind of architecture of these neural networks, you can pick out different components. So I mean, these current large language models are all used in neural models. It's called the transformer, that it has components inside it of residual connections and fully connected layers and attention layers, and you can point to all of the places that they came from from prior work.

( 08:32 ): But despite that all until, let's say 2017, people realized that language models had an important role for fluency of texts, predicting what's likely in speech recognition, but no one thought this is going to be the way to achieve language understanding or to achieve the ability to generate whole passages of text, tell an entire story. It was sort of seen as for low level stuff of predicting a few words around each other. And so it was just very unexpected that this direction that emerged in 2018 that if you just make these neural language models very big, they just start to generate amazing capabilities.

( 09:26 ): And at least so far for the trajectory that we've been in the last five years, as you make these models bigger and bigger, you just start to see more and more amazing capabilities emerging seemingly from nowhere. So people often talk about emergent capabilities, meaning that we're just building this bigger and bigger word prediction machine, and yet suddenly these models start having a lot of knowledge about the world knowledge about human languages, ability to do things like translate, summarize, et cetera. And I think everybody in the field hadn't expected that, and it was just surprising how this started to happen. And so then it's sort of been a goldmine because once you've found where the vein is, you keep digging as fast as you can in that direction.

Russ Altman ( 10:17 ): Yes, great. So from your perspective, and so I've seen reports with literally hundreds of pages of examples of remarkable things that this ChatGPT-4 in particular can do. Somebody asked it to do a mathematical proof but express the proof as a poem that rhymes, and it did that, it created a rhyming poem that made a mathematical proof. And a lot of these, you could say that they're parlor tricks, but I think that's doing probably injustice to the technology, from your perspective as a computational linguistic, and I know that this is a hard question, what are the one or two capabilities that you're most impressed by in these large language models that you've seen in the last few months?

Christopher Manning ( 11:00 ): Yeah. So I do think the sort of more kind of fun examples you see in the newspaper really do show the ability of these models to put stuff together in creative and clearly original ways. So a lot of them are fun, and I don't think of them as parlor tricks, but in terms of what seems to me special, I mean quite a bit of what I'm seeing as special in the most recent models is that these models do seem to be starting to develop the beginnings of a model of the world where they're maintaining the scenario in their head and can reason with it. So early on people used to say, "Oh, well, yeah, these models are very good at completing sentences and that they can tell a bit of a story and well by the time they could write a five paragraph story and it actually made sense, it kind of continued along coherently in a plausible way that was interesting to read with creative details."

( 12:13 ): That, to me, was already amazing because not that long ago we thought we were doing well on natural language generation if we generated one reasonable sounding sentence, the idea that we could continue through 20 sentences in a five paragraph story and it would all follow from each other and make sense, that seemed a completely out of sight ability for natural language generation.

( 12:41 ): But we are now getting more than that, right? That in the earliest smaller models that if you set up scenarios where the model had to maintain a good understanding of the world and be able to reason about it so that you've started with some facts about John knows some facts about their mortgage, and John's afraid to tell his wife because she will be concerned about X, and then John tells this other person. And so you're starting to put together this complex world model that the language model is also putting together some kind of model of the world. So you can then ask inferences based on that world model as to when the friend meets John's wife, what should they do and what concerns will they have? And the model can answer with the same kind of ability to reason about situations as a human being could.

Russ Altman ( 13:54 ): Of course, we can't go into the details of how these models are built and I don't want to, but they have seen incredible volumes of human generated text. And as you're saying, they seem to have learned a lot more than we would've expected about even human relationships and how they work and how unstated motives might play out in a scenario.

( 14:16 ): Let's go to the topic, and this is I'm sure where you're getting your research agenda for the future, what are the things that it's not doing well or where are the things that we really need to focus attention as these things continue to be rolled out and really made available? I think I read that it's been the most quickly adopted technology in terms of the number of people who have signed up to use it either in their personal life or their professional life. So what should we be worried about? What is it not doing well that might not be in all the advertising material?

Christopher Manning ( 14:48 ): Fair enough. So perhaps the first thing that's been very represented in people saying be worried about these models is, I mean it's commonly called hallucination. I think that's sort of a bad term, but these models will just make stuff up. Now, there are some humans that just make stuff up, we've probably all-

Russ Altman ( 15:15 ): They're often in the news.

Christopher Manning ( 15:17 ): ... seen a few of them, yes, 2017 to 20 or something. But in general, human beings have a pretty good sense of what they know and in certain circumstances they'll tell a yarn, but most of the time they know what they know and will be reporting truthful things in that space. It's actually a kind of deep architectural fact about how these models are designed is that the whole goal of them is given this context, predict what's most likely to follow. And that means if they know facts that they will basically give those facts. But if they don't know facts, or you give them some kind of counterfactual scenario, they will, with equal confidence in the way they talk, just put whatever. So maybe the model doesn't know much about your educational background, Russ, so it'll say, okay, Stanford professor, well maybe that means you got your PhD from MIT say, and well, maybe you had your first job at Columbia and then you moved to Stanford, it'll just write this biography of you-

Russ Altman ( 16:28 ): Just for the record, none of that is true.

Christopher Manning ( 16:30 ): ... as if it were all true, completely straight-faced, but it's just making stuff up. So that's one huge problem to solve. I think we don't currently have a very good idea as to how to solve that. Engineers are clever at refining things and improving metrics, so I think we'll certainly see the amount of stuff made up starting to decrease as people do iterate on these models. But it's very central to the current architecture that these models just in any circumstance put next what's most plausible. So I think we do actually need some more profound architectural advances. I mean, one of the problems with these models is that they are essentially just feed forward models. So at any point they're running stuff through a neural network, generating the next word, and repeating over. And so something people are starting to experiment on is reflective models where stuff feeds back into itself again.

( 17:35 ): And that might give it much more ability to deal with some of this invention. I mean, the kind of interesting fact is if you take one of these great models like ChatGPT or GPT-4, and you show it something that just generated and you then just ask it, how sure are you that this is true? It's actually pretty good at answering that, right? It can often differentiate what it just invented versus stuff that is true. And that's because really internal to its probabilities from its trading data, it does have more idea as to what things are well-supported versus what things are made up. So we kind of need to have an architecture where it can be more using that knowledge that it even has at the time it's generating to differentiate things. And so that leads on to this kind of concept mentioned before of world models.

( 18:34 ): So human beings internal to their head have a model of the world. That's exactly what we play out when I say, "Okay, I have to go and talk to my department chair saying I want to go on leave again, gee, they're probably going to react I was on leave two years ago, and so I'd better come up with a good explanation of why it'd be okay for me to disappear for another six months." We have a model of the world and of people in the world, and we can sort of use that to plan out and put things together in all sorts of ways.

( 19:09 ): Now these large language models, on the one hand are starting to develop models of the world, we mentioned that before. And in fact, basically they're now the best computational models of the world that we have. So people in robotics are starting to use large language models as world models because they actually help them predict what actions that will happen in the world with different objects and different people. But our current models still have very poor world models. And so somehow working out how to have better world models and maintenance of world models in these models. So there's still lots of research directions, we shouldn't give up quite yet.

Russ Altman ( 19:52 ): Great. Well, this is The Future of Everything with Russ Altman. We'll have more with Chris Manning next.

( 19:57 ): Welcome back to The Future of Everything. I'm Russ Altman and I'm speaking with Professor Chris Manning of Stanford University. In the last segment, Chris told us about the complex interaction between intelligence, language, and computer science. He told us that ChatGPT-4 was kind of a huge surprise even for the experts. In this segment he will tell us about some of the risks of this technology, and we'll also go back to translation and talk about what is our current capability for translating between languages, and how are we doing for the old languages that are in many cases disappearing because their speakers are learning other languages instead.

( 20:36 ): I want to start out in this segment just asking you about what you conceive of as the major risks of these technologies. You talked about what the technical challenges to making them better are, separate from that, what could go wrong in our use of these technologies?

Christopher Manning ( 20:52 ): Yeah. So there are lots of risks and lots of things that could go wrong, some of them are very immediate and direct. So these models provide a very cheap way to produce large amounts of text and they can be fine-tuned to produce texts, texts that works the best to influence people. So the industry of advertising has been involved for close to a century now, I guess, with writing texts that influences people to buy certain products or to vote for a certain person. But that's been, relatively speaking, expensive work for you to pay to get people to do. And we are facing the chance now that we'll be able to have these models do that kind of work, not only about two orders of magnitude cheaper, a hundred times cheaper, but actually much better because using our machine learning technology, we can continue to tune these models and have individualized models. So there's a model that's especially good at persuading Russ Altman who to vote for in the next election.

( 22:09 ): And already there are sort of problems with people being too influenced by both advertising and populist opinion. And if that has made much worse, that's potentially quite bad for society.

Russ Altman ( 22:26 ): Wow.

Christopher Manning ( 22:26 ): Yeah, there are other risks as well. I mean some of the other ... so there's influencing people, there are other forms of that. There's disinformation coming, whether from state actors or large companies wanting to influence public opinion, but there's then also concerns that come from biases that might be built into these models. So the fact is these models are dominated by the people who tend to have power because they're by and large the people who write the most and gets disseminated the most. So they're not equally representing all the voices of humanity.

( 23:08 ): And so that is then a bad source of bias, which can further drive things away from the direction of trying to give us equality in society. And that's especially worrying because, at least on the trajectory we are on at the moment, there is a small number of large language models that are dominating the scenes, such as the ones from OpenAI. And so if every ... well to first approximation, everybody is using the same models and they've got particular biases in favor of certain kinds of people and against other kinds of people, well, it's sort of bad if you aren't on the right side of that equation.

Russ Altman ( 23:51 ): Yes. And it sounds to me this is not a purely technical challenge for folks like you and your research colleagues. It sounds like society is going to have to make decisions about what is and isn't allowed, and if there are any kinds of cones that they want to set up for the behavior of these models, and I'm sure that's a difficult discussion. Is it happening?

Christopher Manning ( 24:10 ): It's starting to happen, but I think it's just not happening with the speed and the level of focus on what are quite difficult technical issues that's really needed. Yeah, I think there's just no doubt that we need to be doing more to think about the consequences and regulate what kinds of things are and aren't okay. But certainly in the United States, we're in this situation where most of Congress barely understands how current generation social media works, let alone having the kind of background for sensibly thinking about how to regulate and control these models.

Russ Altman ( 24:54 ): Great. Well, not great, but thank you for those comments. Let me move to an entirely different area, and actually going back to the roots of our discussion in linguistics and computation, I know that one of the big challenges for computational linguistics for many years was translation, translating from English to Spanish, from French to Russian. So let me ask, and it looks like that these models are quite good at translation. As a linguist, what are you seeing there? Is this a solved problem, and at the edges, are there still issues that we have to pay attention to?

Christopher Manning ( 25:27 ): It's definitely not a solved problem, but enormous progress is being made. So I mean, these models are just sort of trained on a lot of text of various languages. So the fact of the matter is just out of the box, you can ask ChatGPT to translate between languages and it does a pretty passable job. It's still the case that people build dedicated neural machine translation models explicitly trained on text to translate, and they're even better. And so we've reached the point for major languages that it's not that everything is always perfect, but translation is just good now, I mean, from those of us at least been around for a fair while and remember the kind of garbage you used to get out of machine translation. I mean, now you can take a paragraph in German, Italian, French, Spanish, stick it into Google Translate and read the English translation, and for first approximation it'll just be perfect. Some other languages like Chinese is still a bit harder.

( 26:32 ): So there's a very good news story. The question is how that extends out to the whole of humanity. So people normally count about 7,000 languages in the world. I mean, a lot of those are unfortunately languages that aren't going to be with us much longer. So, for example, lots of languages in different areas of the planet, including Native American languages and Australian languages, they're down to a handful of people. And although some people are working hard to preserve and reclaim those languages, it seems like there's just no doubt that within a century's time, the number of languages will be down to, well, at least 2000, maybe only 1000.

Russ Altman ( 27:21 ): Wow, so this is a huge contraction.

Christopher Manning ( 27:24 ): So there's going to be a huge contraction in the number of languages spoken in the world, but even if we stick to one or 2000, the fact of the matter is there are only fantastic amounts of data that allow building really good foundation models like we've been talking about for maybe the top 20. I mean, even within the top 20, there's an enormous difference between the amount of data you can collect in English or Chinese versus the amount of data that you can collect in Bangla or Portuguese. And so then things fall off very rapidly get from there. And so there's a real haves and have-nots of this new technology. So it's a mixed story, there's a good news story and a bad news story.

( 28:13 ): The good news story is these models are much better at transferring capabilities across languages than anything we had before. So we're actually sort of making progress and we're also better able to handle smaller languages than we used to be. But that's a relative claim, it's still the case that there are lots of languages with millions of speakers for which we just don't have good language technology, and there's no easy way to make it even in the current world because we just don't have the kind of data resources to be able to do so.

( 28:53 ): And that reflects things like the legacy of colonialism so that you see in much of Africa that there are major languages in Africa, which will have in 10 million plus speakers, lots of people, so that those are larger languages than European languages like Danish or Norwegian. But the fact of the matter is that the educational system in those countries that people are still being schooled in English or French. So these are sort of languages of the community, and because of that, there just isn't the available resources in terms of written materials, et cetera, to be able to build the same kind of language technology.

Russ Altman ( 29:37 ): So in the last 30, 40 seconds, I did want to ask, is there any way that these language models might help this problem? I understand all the ways in which social forces have led to a loss of languages, but is it possible that they will have such a deep understanding of how human language works, that with relatively small bits of language from these dying linguistic traditions that we might be able to regenerate the information because we understand so much about language that, just given one book or one piece of text, that they'll be able to be ... I'm thinking of Jurassic Park, in Jurassic Park they get a little bit of DNA from the T-Rex, and they're able to regenerate the T-Rex. Is there going to be anything like that that you see on the horizon for forensic linguistics?

Christopher Manning ( 30:31 ): Yeah, I think so. And it is already the case that neural models have been applied to deciphering and decoding languages. I mean, I think you can only go so far, right? So you're more expert on DNA than me, but if you have the DNA, that actually is giving you the whole blueprint, so you only need a few strands of DNA and if you've got good enough science to decode it all, you've got everything in some sense. Whereas if you've only got one book or some short stories, you just don't have nearly enough of the language, you just don't know what other words are.

( 31:09 ): So anything that you do in those circumstances can try and be faithful to what you do know and exploit that, but also has to invent stuff. And that's what's happened in human cases as well, so that when Hebrew was in revive for Modern Hebrew, well, certainly it was following what was in Ancient Hebrew, but while people needed a lot of words for stuff that they didn't have words for, and so a lot of new stuff had to be invented, and more extremely the same has been done in some cases of reviving things like Australian Aboriginal languages, which there's only quite partial historical documentation. So you're trying to follow the genius of the language, but you have to be filling, coloring in stuff that is sort of plausible but can't actually claim its fact.

Russ Altman ( 32:05 ): Thanks to Chris Manning, that was the future of computational linguistics. You have been listening to the Future of Everything with Russ Altman. If you enjoy the podcast, please consider subscribing or following it on your favorite app, you'll never be surprised by the future of anything. Maybe tell your friends about it too. Also, of course, rate and review it. It will help us grow and it will help us improve. We have more than 200 episodes in our archives from interviews with people who are inventing the future. Consider checking those out as well. You can connect with me on Twitter @rbaltman, and you can follow Stanford Engineering @stanfordeng.

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• Linguistics Forum

info on grad programs in computational linguistics

By Guest Gnome Chomsky May 25, 2013 in Linguistics Forum

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Guest gnome chomsky.

I just thought I would share my findings with anyone who may find this useful. Also, if anyone has any additional information I would appreciate it. I'm interested in enrolling in a MS/MA program in comp ling. I searched 155 schools in the US that offer any sort of linguistics and narrowed it down to programs with comp ling. Some only accept PhD applicants, some only award certificates, and only a small few (8 to be exact) offer an MS/MA. Here is what I found: 

MS programs: 

1. U of Washington (Seattle campus): very good looking program, only takes one year to complete, is a professional MS in comp ling degree, the most detailed website by far out of any school, it seems they may offer a one-third tuition scholarship, some computer programming knowledge required, easily my top choice. 

2. Syracuse: nice looking program, a few math/logic classes that I find interesting, two years to complete, I talked to a faculty member and was told there is no funding. 

3. Georgetown: looks like one of the best programs out, offers a PhD or MS, very good course listings, website says they do not fund MS students. 

4. Arizona (Tucson campus): MS in Human Language Technology, PhD in comp ling. 

MA programs: 

1. Stanford: I'm not positive if they accept MA students but it appears they do, they offer a PhD as well, very good looking program, still waiting on word about funding. 

2. CUNY Graduate School (Manhattan): looks like a pretty good program, they also offer a PhD it seems, no computer science background needed, I talked to a faculty member and was told they do not offer funding to MA students. 

3. Indiana (Bloomington campus): PhD and MA, it appears they accept MA applicants, I still don't know about funding, I just found out about them yesterday. 

4. Texas (Austin campus): PhD and MA, they accept MA students, not positive about funding but I remember seeing something on their website mentioning assistantships. 

5. Brandeis (about 10 years from Boston): looks like a really good program, I think the highest degree they offer in comp ling is an MA, their website says the average funding per student is 50%. 

Certificates only: 

1. San Jose State: Offer a comp ling grad certificate that can be paired with an MA in linguistics. 

2. San Diego State: Comp ling grad cert can be paired with linguistics MA, certificate is four 3-credit classes. 

3. Montclair State (Montclair, New Jersey): Comp ling certificate can be paired with linguistics MA, but the classes must be taken in addition to the required classes for the linguistics MA. 

4. Colorado (Boulder campus): Certificate in Human Language Technology can be paired with linguistics MA or other MS/MAs, such as computer science. 

5. Eastern Michigan (in Detroit): Certificate in Language Technology can be paired with linguistics MA. 

PhDs only: 

2. UC San Diego

3. UC Santa Barbara

4. Delaware (also have MA in cognitive science) 

5. Maryland-College Park

6. Michigan-Ann Arbor 

7. Minnesota-Twin Cities (seems to have a number of comp ling classes) 

9. NYU (a good amount of comp ling classes) 

10. Ohio State 

Other schools have a few comp ling classes but not a significant enough amount for me to mention. Schools like Carnegie-Mellon and USC have a lot of computer science degrees related to human language but not specifically comp ling degrees. 

• charlemagne88 , nalva , catling and 2 others

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I have some more info on the 5 certificate programs I've listed above. Typically, as is the case with Montclair State, a certificate program is a one-year intensive program, consisting of usually 6 comp ling classes, with the purpose of getting students caught up with the computer science aspects of comp ling. If you were to pair the certificate with the MA, you could still complete them both in the traditional two years since 5 of the 6 classes that count toward the certificate count toward the 12 classes needed for the MA. Plus, you can shape your thesis around comp ling. 

Snuffleupagus

• 5 months later...

So.... Any updates on the funding options for the MAs?

This helped me if anyone's interested:  http://www.linguisticsociety.org/programs

• 6 months later...

Thank you so much in advance for any info that you can provide me with...

Best regards

I lived in Miami for the past 6 years so I know FIU well. I'm starting UW this upcoming fall. I'll be doing the program in Seattle. I have the GI Bill so it's free for me. But it can be pretty expensive. The good news is since it's only a year you're paying less in total than a two year program.

Speaking of that, it only takes one year to complete because it assumes a lot of incoming knowledge, much more than any other comp ling program I've seen. I don't know how much math or computer science knowledge you already have but you can't come into the program without any. I only got a minor in computer science but a lot of people go into the program with a bachelor's in computer science. I had to teach myself a lot of stuff that the minor didn't cover.

Here's how the program works:

After getting accepted you have to take a placement test. It's actually a few months after getting accepted so you'll have time to study. But this isn't stuff that you can't just teach to yourself in a week. There are four parts to the placement test. Each part is a 90 minute test and you can take each test at different times.

Part 1 covers formal language theory. This contains regular/context-free/recursive languages, expressions, grammars, automata, complexity theory, etc.

Part 2 covers probability and statistics for computer science. This is calculus- based probability so it's upper-division level math that you'd take after differential and integral calculus. Some should would call this stochastic models. It also requires knowledge of discrete math such as combinatorial questions.

Part 3 covers miscellaneous questions in Unix, linguistics, and data structures. Unix is the commands you would need to know to sort through files in directories and perform operations. Linguistics is focused mainly on the syntax side and drawing parser trees. Data structures is probably advanced data structures questions and run time.

Part 4 covers programming. This is to see how well you can code. Any language of your choice. It gives you various problems and you write the code in their compiler.

After you take each test it'll tell you your results. If you pass all four parts you'll be able to complete the program in a year. If you do okay on the tests but not great you'll have to take a refresher course over the summer. But if you do poorly you'll have to take prerequisites before beginning the program and the program will take you more than a year to finish.

If you have little or no math or programming experience you'd be better off taking the prerequisites at FIU. It'll be much cheaper. I came in with no experience and spent the last year and a half taking the pre reqs at my undergrad university.

• lingua_ex_machina and catling

I don't know a great deal about the HLT program at AZ or specifically the PhD in comp ling there, but I'm an incoming Anth/Linguistics phd student there and the department as a whole, I have to say seems really great. I specifically remember meeting the faculty member who is in charge of the HLT program during my campus visit and he's a great guy, the rest of the faculty I met with were all top notch. I'm not sure that they fund the HLT masters, they probably don't or you'd be low on the funding list. Their PhD funding is a little confusing but it's pretty decent for Tucson, which has a low cost of living. Every one of the prospective students that came to the visit weekend that I was in contact with said they were incredibly impressed with the Linguistics dept there, even if they ended up deciding to go somewhere. It's a really solid/welcoming dept. 

• 9 months later...

I have been reading this page since I started my application.

I have now been admitted to Syracuse, Brandeis and Georgetown. Cannot really decide which one to go.

Can you give me some advise?

• 1 year later...

lingua_ex_machina

On June 7, 2014 at 6:03 AM, Guest Gnome Chomsky said: Hey nalva,   I lived in Miami for the past 6 years so I know FIU well. I'm starting UW this upcoming fall. I'll be doing the program in Seattle. I have the GI Bill so it's free for me. But it can be pretty expensive. The good news is since it's only a year you're paying less in total than a two year program.   Speaking of that, it only takes one year to complete because it assumes a lot of incoming knowledge, much more than any other comp ling program I've seen. I don't know how much math or computer science knowledge you already have but you can't come into the program without any. I only got a minor in computer science but a lot of people go into the program with a bachelor's in computer science. I had to teach myself a lot of stuff that the minor didn't cover.   Here's how the program works:   After getting accepted you have to take a placement test. It's actually a few months after getting accepted so you'll have time to study. But this isn't stuff that you can't just teach to yourself in a week. There are four parts to the placement test. Each part is a 90 minute test and you can take each test at different times.   Part 1 covers formal language theory. This contains regular/context-free/recursive languages, expressions, grammars, automata, complexity theory, etc.   Part 2 covers probability and statistics for computer science. This is calculus- based probability so it's upper-division level math that you'd take after differential and integral calculus. Some should would call this stochastic models. It also requires knowledge of discrete math such as combinatorial questions.   Part 3 covers miscellaneous questions in Unix, linguistics, and data structures. Unix is the commands you would need to know to sort through files in directories and perform operations. Linguistics is focused mainly on the syntax side and drawing parser trees. Data structures is probably advanced data structures questions and run time.   Part 4 covers programming. This is to see how well you can code. Any language of your choice. It gives you various problems and you write the code in their compiler.   After you take each test it'll tell you your results. If you pass all four parts you'll be able to complete the program in a year. If you do okay on the tests but not great you'll have to take a refresher course over the summer. But if you do poorly you'll have to take prerequisites before beginning the program and the program will take you more than a year to finish.   If you have little or no math or programming experience you'd be better off taking the prerequisites at FIU. It'll be much cheaper. I came in with no experience and spent the last year and a half taking the pre reqs at my undergrad university.

Any updates? Either way, this was really useful to me, as it made me understand that if you're not necessarily ready from the technical side of things, that they will simply place you accordingly to catch you up.

Thanks for this great info! I am really interested in doing a certificate in computational linguistics (and maybe even an MS), but I can't find any that are completely online. I know the UW Master's can be done online, but I have no math background and only recently started learning to program in Python so I would need to take several courses before I would even be considered. I'm actually most interested in corpus linguistics and its applications to teaching foreign languages and materials design, but I can't find any programs specifically for that - though I am only looking in the US for now.

• 3 weeks later...

I'm a current undergrad student hoping to do a master's in computational linguistics/NLP. The more I read about it, the more I'm drawn to the University of Washington's MS program, but I'm rather worried because my GPA isn't great... around 3.3 at McGill University in a cognitive science program (though McGill has no cognitive science courses, so a majority of the courses I've taken for the 54-credit program have been in linguistics and computer science). However, that also includes six semesters of Arabic courses, in which I did quite well; if you drop those and look only at the linguistics and computer science courses, the average is closer to 3.0.

Am I crazy even to be thinking of applying to this program? How about the other programs (either MS/MA)? It's very difficult to find information on what sorts of applicants get accepted... Also, in the case that this isn't really a possibility for me right out of undergrad, what sorts of steps might I be looking at taking in order to strengthen my application with an eye to getting accepted the following year? Or, for that matter, what steps should I consider taking now in order to make the most of this upcoming year, my last of undergrad, in order to improve my chances the first time around?

historicallinguist

On May 20, 2016 at 2:05 AM, nickcsd said: I'm a current undergrad student hoping to do a master's in computational linguistics/NLP. The more I read about it, the more I'm drawn to the University of Washington's MS program, but I'm rather worried because my GPA isn't great... around 3.3 at McGill University in a cognitive science program (though McGill has no cognitive science courses, so a majority of the courses I've taken for the 54-credit program have been in linguistics and computer science). However, that also includes six semesters of Arabic courses, in which I did quite well; if you drop those and look only at the linguistics and computer science courses, the average is closer to 3.0. Am I crazy even to be thinking of applying to this program? How about the other programs (either MS/MA)? It's very difficult to find information on what sorts of applicants get accepted... Also, in the case that this isn't really a possibility for me right out of undergrad, what sorts of steps might I be looking at taking in order to strengthen my application with an eye to getting accepted the following year? Or, for that matter, what steps should I consider taking now in order to make the most of this upcoming year, my last of undergrad, in order to improve my chances the first time around?

You said the average is closer to 3.0, if looking at only linguistics and computer science courses. Do you mean that if including everything on the transcript, the cumulative GPA is below 3.0? If that is the case, I would suggest that you try everything you can to make sure that your cumulative GPA above 3.0, because 3.0 is like a threshold, and it is VERY IMPORTANT that you have a GPA higher than that.(it is a necessary but not sufficient condition though. If higher than that, no guarantee for admission. If lower than that, unlikely to get admission unless you have something else that makes you an exceptional candidate, e.g. some journal articles published in Language. 

That said, because McGill's linguistics program is one of the best in the world, if you could get some letters of recommendation from some people from the linguistics department at McGill, and if the letters from these people are positive, it may be possible that your low GPA will be substantially boasted by the letters from these well-known people, and therefore you may still be admitted with a below-threshold GPA. As far as I know, most of the profs. at McGill's linguistics department are exceptionally wonderful people both in terms of their qualification and the quality of the papers they published. I would suggest you to take full advantage of this precious human (and academic) resources available to you.

You said you a in a cognitive science program but there is no cognitive science courses for your program at McGill. I am a bit confused by this. What do you mean? How could there be a program without courses within the program?

The Arabic courses you mentioned were not particularly relevant in this case. When you do write your statement, the emphasis should be placed on theoretical linguistics and computer sciences, not specific languages. After all, the pedagogical grammars of Arabic you studied in the past have very little, if anything, to do with the generative grammar, and formal theory of language (and programming language) you will be concentrating on when you are in the MS program at UW in the future. 

• 1 month later...

Buffalo and Rochester offer MS degrees too. Colorado-Boulder has recently approved one so I'm guessing they'll start it next year.  I'm a recent mechanical engineering graduate in India currently working in data science, and I'm very interested in MS computational linguistics programmes. I'm considering most of the universities mentioned here. I don't really have much experience in linguistics, apart from self-study and a couple of MOOCs. Apart from English, I know three Indian languages and three foreign languages, two at intermediate and one at elementary level. I have studied some programming and am learning to do it well for my job. I have good GRE scores (all above the 90th percentile) and a decent GPA (>8/10) in my degree, if that counts. If there is anyone here from a non-CS, non-ling background who is applying to/has applied to/is studying in computational linguistics programmes, or anyone who knows about it, please do tell us about your experience! I'd like to know if I stand a chance here.

On June 30, 2016 at 10:53 AM, gulabjamun said: Buffalo and Rochester offer MS degrees too. Colorado-Boulder has recently approved one so I'm guessing they'll start it next year.  I'm a recent mechanical engineering graduate in India currently working in data science, and I'm very interested in MS computational linguistics programmes. I'm considering most of the universities mentioned here. I don't really have much experience in linguistics, apart from self-study and a couple of MOOCs. Apart from English, I know three Indian languages and three foreign languages, two at intermediate and one at elementary level. I have studied some programming and am learning to do it well for my job. I have good GRE scores (all above the 90th percentile) and a decent GPA (>8/10) in my degree, if that counts. If there is anyone here from a non-CS, non-ling background who is applying to/has applied to/is studying in computational linguistics programmes, or anyone who knows about it, please do tell us about your experience! I'd like to know if I stand a chance here.

UW at Seattle also has a very good MS program for computational linguistics. You can definitely take a look at this one. I was from a non-CS, non-linguistics background, and applied to/will be studying in a theoretical linguistics program this fall. I applied for two application seasons two years ago and last year. One very important experience I have to say is that do not emphasize your skills in pedagogical grammar of specific languages/however many languages you know/studied, because it looks like emphasizing these will do you little service, if not disservice at all, to get admission and funding. I did my first round of application by emphasizing my knowledge in multiple foreign languages, and I was literally get rejected by EVERY linguistics department that year. Then, I reapplied, and switched to focusing my SOP on my academic interest in specific sub-field of linguistics, rather than saying something general about my knowledge as a polyglot/about knowing however many languages. This time worked, and I got accepted with funding.. 

I think GPA and GRE are NOT the most important elements that will determine whether you can get admission and/or funding. They are like threshold. GRE and GPA will matter, not so much for the department (and the ADCOM), but more for university-wide competition that is beyond the purview of what the department can decide. According to your description of your GPA and GRE, they are already above the threshold. So, not so much to worry about these two. I think your focus now should be on SOP and writing sample, as these two things are going to substantially determine whether you get a deal from the admission committee or not.

Finally, instead of saying HOW you get your linguistics knowledge outside your undergraduate curriculum, a more fruitful approach to improving chances of getting offers would be concentrating on formulating some kind of specific questions in your SOP that interest you, and tell the admission committee why these questions are both interesting and important, how you would like to answer these questions in the future, what theoretical frameworks you would like to work in/on, what are some of the potential deficiencies of the theoretical frameworks you propose to work in/on, how to ameliorate the deficiencies, if any, of the current frameworks. Last but not least, do tell how the admission committee how their curriculum, and faculty members can contribute to your research agenda. After all, you want to find a place that not only offers you admission and funding, but also a place that is the most nurturing for your academic development. 

• gulabjamun and slpmads

4 hours ago, historicallinguist said: UW at Seattle also has a very good MS program for computational linguistics. You can definitely take a look at this one. I was from a non-CS, non-linguistics background, and applied to/will be studying in a theoretical linguistics program this fall. I applied for two application seasons two years ago and last year. One very important experience I have to say is that do not emphasize your skills in pedagogical grammar of specific languages/however many languages you know/studied, because it looks like emphasizing these will do you little service, if not disservice at all, to get admission and funding. I did my first round of application by emphasizing my knowledge in multiple foreign languages, and I was literally get rejected by EVERY linguistics department that year. Then, I reapplied, and switched to focusing my SOP on my academic interest in specific sub-field of linguistics, rather than saying something general about my knowledge as a polyglot/about knowing however many languages. This time worked, and I got accepted with funding..  I think GPA and GRE are NOT the most important elements that will determine whether you can get admission and/or funding. They are like threshold. GRE and GPA will matter, not so much for the department (and the ADCOM), but more for university-wide competition that is beyond the purview of what the department can decide. According to your description of your GPA and GRE, they are already above the threshold. So, not so much to worry about these two. I think your focus now should be on SOP and writing sample, as these two things are going to substantially determine whether you get a deal from the admission committee or not. Finally, instead of saying HOW you get your linguistics knowledge outside your undergraduate curriculum, a more fruitful approach to improving chances of getting offers would be concentrating on formulating some kind of specific questions in your SOP that interest you, and tell the admission committee why these questions are both interesting and important, how you would like to answer these questions in the future, what theoretical frameworks you would like to work in/on, what are some of the potential deficiencies of the theoretical frameworks you propose to work in/on, how to ameliorate the deficiencies, if any, of the current frameworks. Last but not least, do tell how the admission committee how their curriculum, and faculty members can contribute to your research agenda. After all, you want to find a place that not only offers you admission and funding, but also a place that is the most nurturing for your academic development. 

That is some great advice, thank you! I will think about my focus and keep that in mind when writing my SOP. Funding is out of the question though, because I'm looking at taught master's programmes that don't offer it. Could you also tell me about your experience with LORs? Obviously I can't get any from professors in CS or linguistics, and not from work either because that would jeopardise my job. I was thinking one from a mechanical engineering professor I worked closely with, one from a Sanskrit/classics professor with experience in natural language processing (I didn't work with him on anything, only took a classics class with him but I've spoken with him a lot about the field and about my writing sample), and one from a foreign language professor, but I'm kind of doubting that now. I don't know if I should get another one from an engineering professor instead, because I don't know if that would help.

On July 2, 2016 at 9:31 AM, gulabjamun said: That is some great advice, thank you! I will think about my focus and keep that in mind when writing my SOP. Funding is out of the question though, because I'm looking at taught master's programmes that don't offer it. Could you also tell me about your experience with LORs? Obviously I can't get any from professors in CS or linguistics, and not from work either because that would jeopardise my job. I was thinking one from a mechanical engineering professor I worked closely with, one from a Sanskrit/classics professor with experience in natural language processing (I didn't work with him on anything, only took a classics class with him but I've spoken with him a lot about the field and about my writing sample), and one from a foreign language professor, but I'm kind of doubting that now. I don't know if I should get another one from an engineering professor instead, because I don't know if that would help.

Professional LORs from your bosses are rarely helpful for graduate admission to a nonprofessional advanced degree whose admission depends on pretty much your academic preparation and suitability rather than work experience (Professional advanced degrees are degrees such as MBA, JD, MD, etc.). I would recommend that you ask for an LOR from your mechanical engineering professor, because this person could say a lot about your mathematics background that is one of the important qualifications the admission committee of the MS program is likely to look for. For the Sanskrit/Classics Prof. you mentioned, it depends. First, I am not sure whether the Sanskrit/Classics Prof. is a philologist or a linguist. You said this Sanskrit/Classics Prof. also had some experience in NLP. What exactly did he do in the field of NLP, and how is his experience in NLP relevant to the coursework you had with him? If you can figure out some sort of connection between his experience in NLP and how his experience in NLP is relevant to your classics coursework, do ask him for an LOR. If not, I would say he is less ideal.(But of course, life is not perfect, and sometimes we have to take advantage of whatever is available even if it is not ideal).  

I do have one other question about the classics class you took with this person. I took classics class when I was an undergraduate. Based on what I know about classics, a classics class could mean quite a number of very different things. Is your classics class a translation class of classics texts, or a classics literature in translation class, or a comparative philology class, or something else? 

Finally, I would strongly recommend that you ask another from an engineering professor instead of your foreign language professor. Here is why. First, what can a foreign language professor say about you as a student? If he/she says something positive about you, he/ she may say something like the following, 

XXX is very good at XXXX language. XXX is a very hard working student dedicated to studying the XXXX language and XXXX culture. XXX is good at reading in XXXX language, having good conversational skills in XXXX language. etc.

So, here is the problem. Whatever the foreign language prof. has to say about you has very little to do with whether you will be successful in a computational linguistics program. Furthermore, in some cases, a foreign language prof., especially if this prof. is trained in education rather than linguistics,  could possibly miss the point of what grammar is in linguistics, and mistake pedagogical grammar as grammar in linguistic sense. I guess this could potentially do you a disservice. So, try to avoid it and ask another engineering Prof. instead. The emphasis of the LOR from engineering Profs, in my opinion, should focus on your problem solving skills, mathematical background, and your potential to do research in computational linguistics in the future. If possible, try to also ask your engineering Prof. to relate in the LOR your undergrad research experience in engineering to your academic goal as a computational linguist. (For instance, how your undergrad research experience prepared you to do computational linguistics, and how the undergrad experience inspired you to extend your horizon to another field, etc)

• 2 weeks later...

Thanks for the information. 

charlemagne88

I have some information about the PhD program at the University of Minnesota- Twin cities. I did my undergrad in linguistics there so I know a little bit about what the faculty there is currently doing. The departments main focus is syntax, however there is one course offered in computational linguistics which focuses on teaching python and one course in semantics which briefly touches lambda expressions. They do offer an MA in linguistics -but the department is extremely small. I'm pretty sure they only admitted one student last fall. And most of the grad students in the program are international students. 

I think you'd have better luck getting in to the U of M if you're interested in doing any research in conversational analysis, anthro-ling, or syntax. 

• 4 weeks later...

On 7/13/2016 at 0:24 PM, charlemagne88 said: I have some information about the PhD program at the University of Minnesota- Twin cities. I did my undergrad in linguistics there so I know a little bit about what the faculty there is currently doing. The departments main focus is syntax, however there is one course offered in computational linguistics which focuses on teaching python and one course in semantics which briefly touches lambda expressions.

FYI one of the people there who I'm guessing taught some of these courses is moving to UCLA this fall.

I need some help and advice.I am planning to apply for MS in Computational Linguistics but my toefl score is 80 only, and is going to appear in GRE this month,so I am bit confused about my chances of getting admission in any university.I did my masters in Linguistics and have been working in this field since last two years.Will that help me? 

7 hours ago, sandipana said: Hi, I need some help and advice.I am planning to apply for MS in Computational Linguistics but my toefl score is 80 only, and is going to appear in GRE this month,so I am bit confused about my chances of getting admission in any university.I did my masters in Linguistics and have been working in this field since last two years.Will that help me? 

What programs do you plan on applying to?  Some will tell you a minimum TOEFL required.  My husband is applying for an MBA and needs anywhere from 70-100 for the programs he's looking at.  If the programs don't specify, this website can be helpful in getting a general idea of what's expected. though it's not program specific or for grad school admissions.  If there isn't a minimum score required, it's less likely that a score of 80 alone will disqualify you from a program if you have a strong application altogether. 

• 3 months later...

Tulane University also has MA program of computational linguistics. 

But I don't know whether it is worth going. 

ThatSillyLinguist

On 6/7/2014 at 4:03 PM, Guest Gnome Chomsky said: After you take each test it'll tell you your results. If you pass all four parts you'll be able to complete the program in a year. If you do okay on the tests but not great you'll have to take a refresher course over the summer. But if you do poorly you'll have to take prerequisites before beginning the program and the program will take you more than a year to finish. If you have little or no math or programming experience you'd be better off taking the prerequisites at FIU. It'll be much cheaper. I came in with no experience and spent the last year and a half taking the pre reqs at my undergrad university.

I'm a current (BA) linguistics major and I'll be applying to grad school in December (For Fall 2018 admission) and am starting to look into programs. My top choice is currently University of Washington's MS in computational linguistics; the program looks phenomenal. That being said, I have no previous experience in any type of computer programming and can't remember the last time I took a math test. Are these subjects prerequisites that must be completed before applying? Or can you take them through the university afterwards? I noticed that they had a two year program which I wouldn't mind doing (instead of graduating in one year)... if I do that, do I still need to take other computer or math courses? Any help would be greatly appreciated!!    

2 hours ago, ThatSillyLinguist said: I'm a current (BA) linguistics major and I'll be applying to grad school in December (For Fall 2018 admission) and am starting to look into programs. My top choice is currently University of Washington's MS in computational linguistics; the program looks phenomenal. That being said, I have no previous experience in any type of computer programming and can't remember the last time I took a math test. Are these subjects prerequisites that must be completed before applying? Or can you take them through the university afterwards? I noticed that they had a two year program which I wouldn't mind doing (instead of graduating in one year)... if I do that, do I still need to take other computer or math courses? Any help would be greatly appreciated!!    

They have a lot of information on their website about admissions and prerequisites. You do need to know how to program to apply for the one year program. The pathway for linguistics majors allows you to complete the prerequisites and complete the program in two years, but you still should know how to program.  You have a year, so take an intro to programming course and a statistics class if you haven't already.  Focus on learning either Java or C++ as well as you can in the next year, but you don't need to be an expert as it is expected for the 1 year applicants. 

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2100 accredited US Universities for Graduate Programs. 400+ specializations.

1400 No GRE, GMAT schools for the Master’s program

700+ Graduate scholarships totaling $3.5 mm

Top Masters Programs in Computational Linguistics

Graduate degree in Computational Linguistics is offered by 57 American universities.

Massachusetts Institute of Technology

This course is the third and final part of our graduate introduction to semantics. The other two classes are 24.970 Introduction to Semantics and 24.973 Advanced Semantics. The semester will be divided into somewhat independent units.

This course is the third and final part of our graduate introduction to semantics. The other two classes are 24.970 Introduction to Semantics and 24.973 Advanced Semantics. The semester will be divided into somewhat independent units. One unit will be devoted to conversational implicatures (mainly scalar implicatures) and another to presupposition. In each unit, we will discuss basic concepts and technical tools and then devote some time to recent work which illustrates their application.

Pragmatics in Linguistic Theory

• GRE Required:  Yes
• Research Assistantships:  2473
• Teaching Assistantships:  711
• Financial Aid: Register to view the details

Yale University

Students in the doctoral program who complete all requirements for the Ph.D. apart from the submission of a completed dissertation (but including the presentation and successful defense of a dissertation prospectus) may petition for the M.A. degree.

Students who successfully complete the course work, examinations, and work samples required by the end of the second year of graduate study were awarded an M.A.

Yale Linguistics

• Research Assistantships:  1565
• Teaching Assistantships:  1598

Harvard University

PhD Students in the Humanities and Social Sciences (FAS).

The Linguistics offers two secondary fields, one in Historical Linguistics and the other in Linguistic Theory, for linguistics graduate students and for graduate students not enrolled in the linguistics Ph.D program.

Historical linguistics, the study of how languages change over time, subsumes both the general study of language change and the history of specific languages and language families. At Harvard, the theoretical aspects of historical linguistics are covered in courses offered by the Linguistics, while courses dealing with the historical linguistics of specific languages are offered both by the Linguistics and the relevant language departments.

Linguistic theory, the core of the modern field of linguistics, seeks to characterize the linguistic knowledge that normal human beings acquire in the course of mastering their native language between the ages of one and five. Studied as an internalized formal system, language is a source of insight into a wide range of human pursuits and abilities, some of them traditionally approached through the humanities, others through the social sciences, and others through the behavioral and natural sciences. The major divisions of linguistic theory are syntax, the study of sentence structure phonology, the study of sounds and sound systems morphology, the study of word structure and semantics the study of meaning. Courses in these areas regularly draw students from other Harvard departments, especially Psychology, Philosophy, and other departments associated with the Mind, Brain, Behavior Initiative. The secondary field in Linguistic Theory allows such students to receive official recognition for their linguistics coursework.

The Graduate School of Arts and Sciences - Historical Linguistics

• Research Assistantships:  864
• Teaching Assistantships:  1388

127 universities offer the Master's program in Computational Linguistics.

Which one best suits your need?

Stanford University

We offer an M.A. degree for Stanford graduate students which develops students' knowledge of linguistics, preparing them for a professional career or doctoral study in linguistics or related disciplines.

This is achieved through completion of courses, including coursework in an area of specialization within the field, and experience with independent research in Linguistics.

Only current Stanford graduate students are eligible for this program.

Master for Stanford Graduate Students

• GRE Required:  Register to view the details
• Research Assistantships:  Register to view the details
• Teaching Assistantships:  Register to view the details

University of Chicago

The Linguistics at the University of Chicago is the oldest in the country. Founded in the 1930s, the department is committed to interdisciplinary and interdepartmental study, while still maintaining its distinctive investment in theory and the empirical study of language. In addition to linguistics courses, language courses in American Sign Language, Basque, Modern Greek, and Swahili are taught through the department. MAPH students interested in linguistics may take the bulk of their elective coursework through the department, but they may also be interested in coursework in Comparative Literature, East Asian Languages and Civilizations, and Language Study.

LING 30401 Psycholinguistics: Language Processing (Monica L. Do) This is an advanced introduction to the field of psycholinguistics. Models at both the computational and the mechanistic levels will also be examined.

LING 60 Methods in Gesture and Sign Language Research (Diane Brentari) In this course we will explore methods of research used in the disciplines of linguistics and psychology to investigate sign language and gesture. We will choose a set of canonical topics from the gesture and sign literature such as pointing, use of the body in quotation, and the use of non-manuals, in order to understand the value of various effective methods in current use and the types of research questions they are best equipped to handle.

Philosophers and others have puzzled over this question for millenia. Many have concluded it to be intractable. In recent decades, the field of cognitive science--encompassing philosophy, psychology, neuroscience, computer science, linguistics and other disciplines--has proposed a new form of answer. The driving idea is that the interaction of the mental and the physical may be understood via a third level of analysis: that of the computational. This course offers a critical introduction to the elements of this approach, and surveys some of the alternatives models and theories that fall within it.

LING 31300 Historical Linguistics (N. Tulio Bermúdez) This course deals with the issue of variation and change in language. Topics include types, rates, and explanations of change the differentiation of dialects and languages over time determination and classification of historical relationships among languages, and reconstruction of ancestral stages parallels with cultural and genetic evolutionary theory and implications for the description and explanation of language in general.

Master of Arts Program in the Humanities

University of pennsylvania.

The Master degree provides a grounding in the core areas of linguistic theory, supplemented by courses chosen according to the student specific interests. The Graduate Group in Linguistics does not provide fellowships for pursuit of a master degree, so students must bring funding from another source. We do not offer conditional admission.

The admission committee looks for applicants to demonstrate an appropriate academic background to pursue specialized research in formal linguistics, as well as showing interests that match the research pursued at Penn. Note that work on applied and educational linguistics at Penn is centered in the Graduate School of Education, and not in the School of Arts and Sciences.

The requirements for the M.A. degree are the following at least three semesters in residence are necessary to complete them, but a full two years is the normal length of time, with the final semester spent focused on the thesis.

Satisfactory completion of eleven approved course units (one semester each), including one year each of phonology - Ling 5310-5320 (Ling 530-531) and syntax - Ling 5510-5520 (Ling 550-551).

The current tuition rates and fees for students in either the PhD or MA program are available from the graduate division.

Students must submit an on-line application no later than the spring semester of the junior year the December deadline for graduate applications is not relevant in this case. The components of the application are the same as for the M.A. except that submatriculants should arrange only two letters of recommendation. As with our other graduate programs, submatriculants do not need to include GRE scores TOEFL scores are also not required.

Be aware that, due to University rules, students cannot earn graduate credit until they are actually enrolled in a graduate program. For this reason, students intending to submatriculate must submit an application early enough that they can be admitted before the end of the first semester in which they take a graduate course that is intended to be counted toward the M.A. Beginning Spring 2023, applicants aiming to count a graduate course they are already enrolled in are strongly recommended to apply by the last day of the Drop Period in the semester in which they are taking that course, in order to ensure that the application can be evaluated in time for the course to count.

Master’s Degree

Northwestern university.

I hold a PhD in Linguistics from the University of Chicago and a BA in Linguistics from the University of Cambridge.

Address Northwestern University Linguistics 2016 Sheridan Rd, Room 205 Evanston, IL 60208.

Eszter Ronai

Brown university.

Brown University Cognitive, Linguistic, and Psychological Sciences (CLPS) is dedicated to the multidisciplinary study of mind, brain, behavior and language.

The department offers three Ph.D. programs: in Cognitive Science, Linguistics and Psychology. Ph.D. students are accepted by the department and formally choose one of the three programs. The department does not accept students interested in obtaining terminal master degrees.

Linguistics focuses on the nature of human language: modeling human linguistic knowledge and its theoretical, behavioral, and biological bases. Brown University graduate program in Linguistics is designed to prepare students for careers as scientists and teachers who will make contributions in academic or applied settings. Students will gain broad competence in the scientific issues using formal, theoretical, experimental, and or computational methods relevant to modeling linguistic domains, and are expected to develop expertise in one or research specializations. We especially encourage directions of study that bridge work in theoretical linguistics with experimental and or computational methods. Programs of study are highly individualized.

Students accepted into the Linguistics Ph.D. program are guaranteed five years of contingent on satisfactory progress toward the degree. In addition to the Graduate School doctoral support, the department also typically provides a summer stipend for a fourth summer if the student continues to work on research over that period. Support normally comes in the form of teaching or research assistantships, and students are encouraged to apply for their own fellowships (e.g., NSF) before or after being admitted to the program.

In addition, PhD candidates in linguistics must demonstrate a reading knowledge of one foreign language (usually French, German, or Russian) or knowledge of a foreign language to a level suitable for conducting linguistic research on that language.

Graduate Programs

Cornell university.

Matriculation through the graduate program in Linguistics requires the understanding of procedural guidelines and subsequent academic requirements such as exams, Teaching Assistantships, fellowship opportunities and program requirements. Here we have provided department-specific information and documents you will need throughout your course of study along with graduate school requirements and policies.

This process takes the place of uniform course requirements and uniform departmental examinations. degree programs.

It is the responsibility of the candidate to become familiar with the various regulations that ir program and to satisfy them in the proper way. For the Ph.D. degree program there are three requirements imposed by the Graduate School: registration units examinations and the dissertation or thesis.

Also know that the Graduate Field Assistant is available to lead you to resources on campus.

Italicized sections of this checklist are new Graduate School policies which apply to students beginning with those admitted for Fall 2014.

Students entering 2018 and later are required to give a colloquium-length presentation in the department at some point during their time at Cornell.

Make significant inroads on completing the core courses.

File academic plan with Graduate School describing anticipated summer academic activities and outcomes (due May 1, required for summer funding).

Complete any ancillary skills courses your committee requires (if any).

The Q-Exam should be attempted before the end of the 4th semester. Summer funding for the second summer will be contingent on having attempted the Q-Exam by this deadline. To qualify for summer funding at the end of the fourth semester, it is essential that you schedule your Q-Exam no later than May 1st, and that the date of the exam be no later than May 14th.

Take A-Exam (report results Results for Admission to Candidacy (A Exam), eligibility for 3rd summer funding is contingent on passing A-exam or filing a scheduling form by May 1 indicating an intention to take the exam by the start of the 7th semester).

Apply for dissertation year fellowships (usually done in fall) and other post-A-exam funding (such as East Asia Program fellowships).

4th year summer funding is available by application only students who have not passed their A-exam are not eligible. Applications for summer funding are due May 1 at the Graduate School. Students who have not passed their P-Exam are also ineligible for 4th year summer funding. Exams not completed by May 1st will result in funding being withheld.

Take B-exam (defense of dissertation, report results Results for Final Defense of Ph.D. Degree (B Exam) ).

Beyond the first year Special Committees guide the student. At the beginning of the 3rd semester the student selects a Special Committee to guide in the writing of the Q-Exam paper. At the end of the first semester (by December 1st), 2nd year students should submit a Q-paper proposal to the committee. This paper is defended at the end of the 2nd year. The paper is defended at the end of the 3rd year. The dissertation prospectus must be defended by the end of the fall semester of the 4th year. The dissertation is defended at the B-Exam. The A-Exam and the B-Exam require scheduling and result forms to be filed with the Graduate School, the Q-Exam and P-Exam forms are filed department-internally only.

The Ph.D. in Linguistics is awarded to students who have demonstrated the ability to conduct independent, original research in the field, and have acquired mastery of linguistic concepts, methods and data. Progress towards the degree is attained by: (1) completing the core course requirements (2) passing the Qualifying Exam (Q-Exam), results reported to Field (3) passing the Admission to Candidacy Exam (A-Exam), results reported to Grad School (Results for Admission to Candidacy (A Exam)) (4) defending the prospectus (P-Exam), results reported to Field (5) completing and defending dissertation (B-Exam), results reported to Grad School (Results for Final Defense of Ph.D. Degree (B Exam)).

To assure that Ph.D. students receive an adequate grounding in all of the fundamental areas of linguistics, the field has defined a set of core requirements in the areas of Syntax, Phonology, Semantics, and Historical Linguistics. The general expectation is that all students will take all core courses. Beyond the core courses, Ph.D. students are expected to attend advanced linguistics courses (topics courses and seminars) not only in the areas in which they write their research papers and thesis, but in areas that will provide sufficient breadth as advised by the Special Committee.

Students are required to complete courses equivalent to the following:.

Research Workshop (LING 6603 6604): This course provides students with an opportunity to develop an original research paper through a number of revisions (the Q and A papers are the main focus of attention, in their respective semesters), some of which are presented to an audience of fellow students. The final version is presented at a semester-end mini-conference.

Advanced courses: all students are required to take at least four (4) seminars or topics courses for credit. These are courses at the 6600-level or higher.

In the course of research, a student may need to master one or ancillary skill sets. These might be familiarity with languages of scholarship, or training in statistics, logic, field methods or programming.

A regular course load for students without teaching appointments is four courses. Students who are teaching normally take three courses. their credits by registering for Directed Research. Students may exceptionally take courses, if deemed appropriate by the Special Committee. Aside from the Research Workshop, which is offered only as an S U course, linguistics courses should be taken under the letter grade option. After the A-Exam, additional courses may not be taken for credit (unless required by your committee or fellowship), however, auditing courses is encouraged.

All full-time graduate students are required to be registered for at least 12 credits. All students must register on-line through the student center on-line system, Student Center.

Incompletes for core courses are granted only in exceptional circumstances, and the incomplete must be made up by the beginning of the next semester. This deadline should be taken seriously. After one year, a grade of Incomplete becomes permanently frozen by the Graduate School. Q and A exams cannot be scheduled with any Incompletes still outstanding.

A list of courses in linguistics and related information will be posted on the department bulletin board (by the lounge) and for each semester or you may look at the on-line course roster or courses of study Courses of Study. Many of the advanced courses in the department take the form of one-time-only Linguistics seminars. Any additions to your course schedule after the three week add period or seven week drop period requires a course enrollment petition (General Petition).

At the end of the academic year, every graduate student is required to complete a student progress review to reflect on the progress made throughout the last year and goals for the upcoming year.

Doctoral degree candidates must complete six semesters before the degree is granted. Normally Ph.D. degree candidates take four to five years of full-time study to complete all degree requirements. All requirements for the doctoral degree must be completed within seven years of the first registration in the degree program an extension may be obtained by petition.

These deadlines are to ensure that committees have time to read the materials and that students are not asked to revise up to the last minute. Deviations from these guidelines can, of course, be negotiated with the Special Committee when necessary.

Admission to candidacy in the field of Linguistics consists of writing two research papers, which are evaluated in two exams, the Q-exam and the A-exam. The Q-exam is taken by the end of the second year, and the A-exam is taken by the end of the third year. in each of these cases does not include summer or winter breaks. While exams are sometimes scheduled during summer, the availability of committee members during breaks is highly variable, and you should not assume that you will have this time for exams.

Students should consult with members of their Special Committees well in advance of the examination date the specific expectations. The Q-Exam is scheduled department internally using the Q-exam scheduling form (available from the GFA or the DGS), and the results are reported to the GFA on the results form. The Q-Exam should be attempted by the end of the 4th semester. Summer funding for the second summer will be contingent on having attempted the Q-Exam by this deadline. To qualify for summer funding at the end of the fourth semester, it is essential that you schedule your Q-Exam no later than May 1st, and that the date of the exam be no later than May 14th.

Scheduling of the A-Exam must be done through the Graduate School. You will need to file the schedule of exam (Schedule A Examination and Research Compliance Form) with the graduate school at least seven days prior to taking your exam. Please plan ahead to acquire the proper signatures. Please begin the online results of examination form the morning of your exam (Results for Admission to Candidacy (A Exam)). The A-exam must be attempted by the start of the 7th semester.

Outcomes of exams (Q or A) may be as follows:.

Candidates who pass the A-Exam may be awarded an M.A. without thesis at that time. This is not automatic. The required box must be checked and initialed by the DGS on the results form.

Following successful completion of the A-exam, a Special Committee for the dissertation is selected and the P-exam (defense of the prospectus) is undertaken by the end of the fall of the fourth year. Scheduling of this exam is done department-internally (with the GFA DGS), and does not involve the Graduate School.

Candidates submit their dissertation to The Graduate School online using the Proquest tool. The Graduate School verifies that minimum formatting requirements are met and sends an electronic copy of the thesis document to the candidate special committee for final approval.

All members of the special committee are expected to attend all examinations. At the discretion of the field demonstrated by permission from the DGS and with the agreement of all members of the committee when scheduling an exam, one or committee members may participate from a remote off-campus location via appropriate, high-quality electronic audio and video conferencing. At least one committee member must be located on the University campus with the student during the exam.

They scan the necessary forms to the department or Jenny.

We typically offer guaranteed 5-year full to students we admit into the graduate program, regardless of the student citizenship. Two of those years (SAGE Fellowship: the first-year and the dissertation year in which students are not expected to work as a Teaching Assistant or Research Assistant) are through fellowships, and the other three years are through other sources of support, typically teaching assistantships or research assistantships. The five-year funding package covers: tuition and fees, student health insurance, and a nine-month stipend for living expenses. Funding is contingent on satisfactory academic performance, and beginning with the 2014-15 academic year, the Graduate School has instituted progress requirements for continued funding.

Students will be eligible for the first and second summers of funding only if they file with the Graduate School by May 1 of that year an academic plan describing the anticipated summer academic activities and outcomes. A form will be developed by the Graduate School for this purpose.

Students will be eligible for third-summer funding only if they have passed the A-exam or have filed an exam scheduling form by May 1 of that year that indicates they are scheduled to attempt the A-exam prior to the start of their seventh semester of enrollment, as required by the Code of Legislation, and if they have filed with the Graduate School by May 1 an academic plan describing anticipated summer academic activities and outcomes.

Students will be eligible for fourth-summer funding only after passing the A-exam, and only by application. A student must submit an application to the Graduate School for 4th summer of support by May 1 of that year, describing the scholarly work completed with the 3rd summer of support and stating the academic objectives to be undertaken during the 4th summer. Graduate School staff will review the applications. Students who have not passed their P-Exam are also ineligible for 4th year summer funding. Exams not completed by May 1st will result in funding being withheld.

No portion of the dissertation-year fellowship may be used by the student later than the twelfth semester of enrollment, unless the student had secured external funding in an earlier term, in which case one or two semesters of dissertation-year fellowship may be used after the twelfth semester corresponding with the length of external funding (one semester if one semester external funding had been secured, or two semesters if at least two semesters of external funding had been secured).

The studies of all graduate students are funded in part by Teaching Assistantships (TA). Every effort is made to match teaching assignments with graduate student interests and to make sure that each Teaching Assistant receives a variety of teaching experiences while at Cornell. Teaching assistants work on average 15 hours per week and do not usually exceed 20 hours in any given week.

A student holding a TAship may work a total of 20 hours per week as a combination of the TA responsibilities and employment elsewhere, either on or off-campus. Students holding a University fellowship, external fellowship, or GRA may also be employed on or off-campus for no than 8 hours per week, as long as this does not conflict with the terms of the external funding agreement.

RAs work 15 to 20 hours per week. If the research project directly relates to the student dissertation, then the appointment is a graduate research assistantship (GRA), in which case the time spent on research connected with the project is expected to be significant hours spent on assignments are not tracked.

Knight Institute for Writing in the Disciplines allots the Linguistics department TA-ships for our graduate students to teach First-Year Writing Seminars. The Linguistics has approved courses that are offered as a writing seminar. See the Graduate Field Assistant for the list of seminars. See the Graduate Field Assistant for the form. The Buttrick-Crippen Fellow will spend the fall semester preparing a new First-Year Writing Seminar for the John S. Knight Institute for Writing in the Disciplines and will teach that seminar in the spring.

The Graduate Field now requires all graduate students to apply for external funding at some point in their first four years. Students in the field of Linguistics are encouraged to apply for a variety of fellowships such as the National Science Foundation, and the Social Science Research Council Fellowships. Also, the area programs at Cornell (East Asian, Southeast Asia, South Asia, and European Studies) offer federally supported Foreign Language and Area Studies (FLAS) Fellowships to students whose research focuses on any of these areas.

Many of these non-Cornell sourced external fellowships are intended for students who are citizens or permanent residents of the United States. Applicants from foreign countries should seek aid from their own governments, universities, or corporations or from a U.S. agency operating abroad, such as the Institute for International Education or the Fulbright-Hays Program. Under certain conditions, external funds can be used to extend the package of guaranteed support from the Field or used in place of the teaching assistantship or research apprenticeship to allow the recipient to focus on research.

The East Asian Program offers the following fellowships that have no citizenship restrictions. These typically provide tuition and stipend for one semester.

Cornell Foreign Language and Area Studies (FLAS), Fulbright-Hayes Awards, Fulbright Program, International Research Travel Grants: The Mario Einaudi Center and its associated Programs offer a wide range of support and assistance to graduate students in search of funding for their international research, study, and scholarship.

The National Science Foundation funds research and education in most fields of science and engineering. For US citizens and permanent residents, these are very competitive, but they provide a multi-year package of fellowship funding. College seniors, first and second-year students with no than 12 months of graduate study (i.e. no MA MS degree) are eligible. It is most advisable to apply in your first year, if you are eligible. Even if you feel you do not have much linguistics research experience, the experience of writing the proposal is worthwhile. You will also get feedback from the NSF Fellowship Panel, which you can incorporate into an improved application the following year, if you do not succeed the first time. If you wait until your final year of eligibility to apply, you cannot take a second chance. The Graduate School requires that you apply for external funding at some point you may as well go for this one.

Students who receive summer support must be actively pursuing their academic program over the summer months. Students must register with the Graduate School for the summer in order to receive summer support. Beginning in 2014, students must submit an academic plan to receive summer funding.

In the highly competitive academic job market, prospective employers place considerable weight on evidence of commitment to research.

The Graduate School supports graduate students for one conference per academic year. You will need to submit the conference grant application (form F6 with all required signatures, brief statement of relevance of conference with research, one page abstract of presenting material acceptance email from a conference agent. You should application prior to the conference, however applications will be accepted up to 30 days after the START date of the conference. Please note that the deadlines for these awards are firm.

Recipients must be enrolled full-time in a graduate degree program through the Graduate School and be a registered student during the term in which the conference takes place.

Only one award will be considered during the academic year, which is from July 1 through June 30.

The field also has an allotment of money to supplement the Graduate School funding for travel expenses. Please include the title of your paper poster and the notice of your conference acceptance with your request.

Beginning July 2, 2018, you may combine these two $250 awards for ONE lump sum award of $500. You may not receive than $500 from the department during the fiscal year, running from July 1-June 30.

If you are attending virtual conferences, you may still apply for funding to cover registration costs. Please submit costs to Jenny for consideration.

The Graduate School awards a small number of grants for research-related travel in the United States or overseas. Students may also Einaudi Center for additional funds for international travel.

Students who have passed their A-exam prior to initiating their travel are eligible to apply. Forms are due October 1 for fall awards and February 1 for spring and summer awards. Awards are announced in December and March.

The Mario Einaudi Center and its associated Programs sponsor the International Research Travel Grants. These grants provide travel support for Cornell University graduate students conducting short-term research and or fieldwork in countries outside the United States. The deadline is the first work day in February.

Students are expected to participate in the intellectual life of the department.

The Cornell Linguistics Circle (CLC) is the graduate student group of the Cornell Linguistics. The CLC serves to promote exchange of ideas among graduate students in the field and to advocate for the graduate student body within the department.

By building computational models to predict human language processing behavior (e.g., reading times), we can study the linguistic features that impact human processing decisions. Relatedly, C.Psyd members use psycholinguistic techniques to study the strategies used by neural networks to produce high accuracy in different language contexts, which gives us insights as to when different strategies might be employed by humans.

At the Linguistic Meaning (LiMe) Lab we investigate the complex process by which humans assign meaning to utterances. To do so, we combine insights from linguistic theory and cognitive science broadly with experimental and computational methods.

Guide to Graduate Study

Columbia university in the city of new york.

Conduct groundbreaking scholarly research and make a global impact as a leader in the field with this 90-point Doctor of Education. Focus your Ed.D. by choosing one of three tracks in Applied Linguistics: second language acquisition, second language assessment, or language use.

Online Degree Application, including Statement of Purpose and Resume.

The Applied Linguistics and TESOL Program offers a Doctor of Education degree with three areas of specialization: language use, second language acquisition, and second language assessment.

Doctoral students have five types of requirements: (1) doctoral candidacy requirements, (2) area of specialization course requirements, (3) elective courses, (4) required out-of-program breadth courses, (5) research and statistics courses.

Students with an M.A. or Ed.M. from Teachers College can use up to 60 prior TC credits toward their Ed.D. program. Students from other institutions may be eligible to transfer up to 45 points from previous graduate study at an accredited institution.

Candidacy Requirements for All Ed.D. Students (Minimum 15 points courses may be taken several times):.

A HL 5507: Research paper in applied linguistics (3).

A HL 6507 Doctoral seminar in applied linguistics (3) (Closed-Book Certification Exam).

A HL 6507-B: Doctoral seminar in applied linguistics (3) (Qualifying Paper Pilot Study).

A HL 7507: Dissertation seminar in applied linguistics (3) (Dissertation Proposal).

A HL 8907: Dissertation advisement in TESOL and applied linguistics (0) (Dissertation Submission).

Conceptualizing and carrying out a pilot study in the area of inquiry (6500 Part B: Qualifying Paper).

Doctoral students are required to demonstrate research preparedness in a substantive area by passing the research paper at the 5500 level, the certification exam and pilot study at the 6500 level, the dissertation proposal at the 7500 level, and the completed dissertation at the 8000 level. Students are expected to make steady and continuous progress toward the degree. Students who fail to make timely progress may be asked to leave the program.

Students who receive less than a B+ on the 5500 paper will be terminated from the doctoral program, and will receive an Ed.M. if their 5500 paper is of acceptable quality. Students may retake 6500 part A or B one time. If students do not pass the proposal or the proposal defense, they will be asked to leave the program.

Doctor of Education in Applied Linguistics or TESOL (90 pts The Second Language Acquisition Area of Specialization).

The second language acquisition area of specialization requires a minimum of 90 graduate credits beyond the bachelor degree.

A HL 5575 Research literacy in applied linguistics and TESOL (3).

Elective courses in Applied Linguistics and TESOL (9 points):.

A HL 5090: Computational Linguistics for Applied Linguists (3).

A HL 5515: Adv.Topics: Applied Linguistics I: Introduction to Corpus Linguistics (3).

Doctor of Education in Applied Linguistics or TESOL (90 pts The Second Assessment Area of Specialization).

The second language assessment area of specialization requires a minimum of 90 graduate credits beyond the bachelor degree.

A HL 6407: Internship in applied linguistics and TESOL: Assessment lab (3).

A HL 5575: Research literacy in applied linguistics and TESOL (3).

Doctor of Education in Applied Linguistics or TESOL (90 pts The Language Use Area of Specialization).

The language use Area of Specialization requires a minimum of 90 graduate points beyond the bachelor degree.

To ensure that students have the skills to do scholarly research in an Area of Specialization, they are required to take a minimum of 6 points in research methods, statistics, and measurement. Depending on the type of dissertation, they may be asked to take additional courses in linguistic analysis, qualitative methods, or quantitative methods.

Elective and Breadth Course Requirements for Language Use (15 points):.

Elective courses or workshops in Applied Linguistics and TESOL (9 points):.

Required out-of-program breadth courses (6 points of any combination):.

Applied Linguistics TESOL

What kind of scholarships are available for graduate programs in computational linguistics.

We have 155 scholarships awarding up to $1,270,915 for Masters program in for Computational Linguistics, targeting diverse candidates and not restricted to state or school-based programs.

Find scholarships and financial aid for Computational Linguistics graduate programs

Which are the accredited universities that offer phd/doctoral programs offered in Computational Linguistics?

39 universities offer graduate PHD program in Computational Linguistics

How much does it cost to get a Master's in Computational Linguistics and how to find the most affordable Masters program?

Master's degree in Computational Linguistics is offered by 95 US universities. The tuition for the Master's degree can range from $15,618 per year at University of North Texas to $57,666 at Boston University.

The tuition at public universities will be lower for in-state students when compared to private universities but you get more financial aid at private universities.

Are there any one year masters programs in Computational Linguistics?

2 Universities offer On-campus Masters Program within an One Year - 18 months. The tuition for Master's can range from $14,549 to $50,505.

Are there universities offering online Master's in Computational Linguistics?

Best Online Masters Programs in Computational Linguistics - Updated 2023 Online Master's in Computational Linguistics

What is the GRE score required for admission to Master's degree in Computational Linguistics?

Gre score requirements differ from school to school. Most colleges do not publish the cutoff scores. For example 99 universities offer Master's programs in Computational Linguistics.

Yale University: The Yale University does not consider GRE scores in evaluating applications.

Columbia University in the City of New York: For Teaching Residents and Peace Corps applicants ONLY: The GRE is required for applicants to these tracks.

Are there colleges for the Computational Linguistics Masters program that do not require GRE/GMAT?

Quite a few accredited universities have waived off the GRE score requirements for admissions to Masters programs. 99 offer Graduate programs in Computational Linguistics. Below are listed 10 universities that do not require GRE/GMAT for admission to Master's program. For viewing the all the schools that have waived off GRE/GMAT for the admission, use Match Me Masters .

Is it worth getting a master's degree in Computational Linguistics?

Before you invest 2-3 years of your life and anywhere between $40,000 - $110,00 of your hard earned money, students do ask as to what is the return on investment on the Master's degree. Here are some of the statistics from bls.gov.

What is Computational Linguistics used for?

The construction of grammatical and semantic frameworks for describing languages in ways that enable computationally tractable implementations of syntactic and semantic analysis are among the theoretical goals of computational linguistics. Translating text from one language to another, retrieving text that relates to a specific topic, analyzing text or spoken language for context, sentiment, or other affective qualities, summarizing text, and building dialogue agents capable of completing complex tasks like making a purchase, planning a trip, or scheduling maintenance and answering questions are all examples of computational linguistics.

What are the fundamental differences between Natural Language Processing and Computational Linguistics?

The discipline's major goal is to model natural languages computationally. Natural language processing, on the other hand, entailed the application of computational engineering approaches to the processing of natural languages. Natural Language Processing and Computational Linguistics both make use of formal training in CS, linguistics, and machine learning. The two tasks are frequently performed by the same people at different times. It's quite rare for a piece of work to be both good NLP and good CL at the same time.

How long will it take to become a Computational Linguist?

To work as a Computational Linguist, you'll need a bachelor's degree and professional experience designing natural language software in a commercial setting, as well as a master's or doctoral degree in a discipline relevant to computer science, which can take anywhere from 2 to 6 years to complete.

What are the career opportunities available for a Computational Linguistics Masters degree student?

Students holding a master's degree in computational linguistics work in universities, government research institutes, and large corporations. Computational linguists can find internships, part-time, full-time, and contract positions with a variety of small, medium, and big companies that develop software, hardware, websites, databases, and digital storage. Linguistics professionals will have more opportunity to aid developers in improving Internet search engines, creating virtual assistants, and integrating speech recognition with other language processing techniques.

Speech data assessors, linguistic data managers, annotators, and localization specialists are examples of linguistics positions in the software business that do not require extensive programming experience.

The average base pay for a Computational Linguist is $90,230 per year.

Get more details on Jobs, Salaries and Career after Masters in Computational Linguistics .

Masters’ program in Physics: Are they hybrid or in-person?

Most institutions have paused lessons because to Covid 19, yet they are still being taught online. You can attend the class in person as soon as the university reopens.

Can I even get the Masters degree in Computational Linguist Online?

Yes, there are universities that offer 100% online masters programs in Computational Linguist.

Are there universities that offer a Masters program in Computational Linguist without any GRE requirements?

GRE is one of the most crucial prerequisites for admission to an authorized university in the United States. But still there are universities that offer Masters programs that don’t require GRE for admission. Check Masters programs in Computational Linguist which do not require GRE for more details.

What kind of scholarships and financial aid are available for programs in Computational Linguist?

For students pursuing a master's degree at their institution, most colleges provide program-specific scholarships. Scholarship amounts differ from one university to the next. Students can also seek assistance from a variety of other sources, which provide scholarships, loans, and awards based on achievement and need.

See Scholarships and financial aid for graduate program in Computational Linguist for more information.

Career Outlook

Overall employment of postsecondary teachers is projected to grow 12 percent from 2020 to 2030, faster than the average for all occupations. About 139,600 openings for postsecondary teachers are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

The median annual wage for postsecondary teachers was $80,560 in May 2020. Number of Jobs in 2020 was 1,276,900.

Career Opportunities with master's degree in Computational Linguistics

How can I compare the Computational Linguistics Graduate Programs?

Compare the GRE score requirements, admission details, credit requirements and tuition for the Master's Program, from 127 universities offering Graduate School Programs in Computational Linguistics. Compare Graduate School Programs in Computational Linguistics

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31 Best universities for Linguistics in Moscow, Russia

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in Moscow ranked based on their research performance in Linguistics. A graph of 183K citations received by 47.6K academic papers made by 31 universities in Moscow was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Moscow State University

For Linguistics

2. National Research University Higher School of Economics

3. RUDN University

4. Moscow Aviation Institute

5. Moscow Institute of Physics and Technology

6. National Research Nuclear University MEPI

7. Moscow State Pedagogical University

8. Moscow State Institute of International Relations

9. Bauman Moscow State Technical University

10. Russian Presidential Academy of National Economy and Public Administration

11. Finance Academy under the Government of the Russian Federation

12. Moscow Medical Academy

13. N.R.U. Moscow Power Engineering Institute

14. Russian State University for the Humanities

15. Moscow State Linguistic University

16. Plekhanov Russian University of Economics

17. New Economic School

18. Russian National Research Medical University

19. National University of Science and Technology "MISIS"

20. Moscow State Technological University "Stankin"

21. Moscow State University of Railway Engineering

22. State University of Management

23. Pushkin State Russian Language Institute

24. Moscow Polytech

25. Mendeleev University of Chemical Technology of Russia

26. Russian State Social University

27. Russian State University of Oil and Gas

28. national research university of electronic technology.

29. Russian State Agricultural University

30. Russian State Geological Prospecting University

31. moscow international university.

Universities for Linguistics near Moscow

Liberal Arts & Social Sciences subfields in Moscow