best nlp phd programs

Joining the Group

The Stanford NLP Group is always on the lookout for budding new computational linguists. Stanford has a great program at the cutting edge of modern computational linguistics.

The best way to get a sense of what goes on in the NLP Group is to look at our research blog , publications , and students' and faculty's homepages . Our research centers around using probabilistic and other machine learning methods over rich linguistic representations in a variety of languages. The group is small, but productive and scientifically focused.

Prospective Graduate Students

Where do you apply for graduate (PhD or MS) study? Not directly to the NLP Group. Stanford graduate admissions are handled through individual departments, so you'll want to apply for admission through either the Linguistics Department or the Computer Science Department . Both departments have excellent graduate programs. Normally, you should apply to the one in which you have more background and greater interest in further study. Do make sure that you emphasize any research experience and results, and that you get letter writers who can speak convincingly about you. Decisions about admissions are made by the department's admissions committee. Because admissions committees represent the whole department and aim to select the best applicants regardless of specialization, you should direct your application towards an appropriately broad audience. And, as you probably know, Stanford admissions are quite competitive.

If you have questions about admissions, please check the graduate admissions web pages listed on the right, or write to the admissions email addresses listed. We NLP Group members attempt to answer specific NLP-related admissions questions (although sometimes we get too busy...), but in general it isn't necessary or helpful to contact us to let us know that you want to apply or have applied for admission.

Current Stanford Students

Are you a student at Stanford and interested in working on a project in NLP? Check out this page for details on how to apply to do research in the group.

Graduate Admissions Resources

Linguistics department, computer science department.

Best Master’s in Computational Linguistics

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Updated October 19, 2023

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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.

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Cambridge, MA

A top-ranked private institution focused on technology, the Massachusetts Institute of Technology offers a doctorate in linguistics that lets students design their own focus area. Since the 1960s when Noam Chomsky and Morris Halle led the linguistics program, MIT has garnered international recognition for its research and scholarship.

After meeting coursework requirements, linguistics students take a three-part general exam, including completing two workshop subjects during coursework, writing two research papers, and passing oral examinations. The requirements ensure that students build broad research strengths in linguistics before focusing on their dissertation.

Program topics and areas of learning at MIT include:

• Experimental linguistics
• Computational linguistics
• Computational modeling of language learning

Applicants must submit a statement of purpose, academic transcripts, three letters of recommendation, and a writing sample. The writing sample should reflect the applicant's ability to conduct scholarly research. Recommended samples include research reports, papers, and theses. All applicants must also provide a sample research summary of their proposed dissertation project.

Stanford, CA

At Stanford University, the Stanford Natural Language Processing (NLP) Group brings together faculty and graduate students in linguistics and computer science to advance the science of computer processing of human languages. Members of the group conduct research on computational linguistics, human language technology, and machine translation.

Graduate students can join the group by gaining admission to the Ph.D. in linguistics program or master's in computational linguistics program. Stanford's linguistics department lets graduate students create an individualized program of study, prioritizing flexibility.

Program topics and areas of learning at Stanford include:

• Computer science with a focus on computational linguists
• Linguistics with a focus on computational linguists
• Natural language processing
• Machine translation

The Ph.D. program receives around 150 applications annually and admits seven students. Both the Ph.D. and master's programs require applicants to submit transcripts, letters of recommendation, a statement of purpose, a resume, and GRE scores.

At Harvard University, graduate students can pursue a focus in computational linguistics through graduate programs in applied computation . The Institute for Applied Computational Science comprises graduate students and faculty focused on applied computational methods, including computational linguistics. The institute emphasizes interdisciplinary collaboration.

Graduate students can choose from several degree options, including two master's degrees in computational science and engineering (CSE), which let students complete independent research projects focused on computation applications. Students can earn a one-year master of science in CSE or a two-year master of engineering in CSE.

Program topics and areas of learning at Harvard include:

• Data science
• Computational science and engineering
• Machine learning

Students interested in computational linguistics can strengthen their skills through a capstone graduate course, which asks students to apply concepts like machine learning to solve real-world problems.

Pittsburgh, PA

At Carnegie Mellon University, the Language Technologies Institute (LTI) trains graduate students in natural language processing, computational linguistics, information extraction, and machine learning. Students can earn a Ph.D. in language and information technology or a master's degree in language technology, intelligent information systems, artificial intelligence, or computational data science.

The two-year master's in language technology incorporates two summers of research. The master's in intelligent information systems lets students concentrate on human language for language technologies, language technology application, or machine learning for language technologies. Doctoral students spend two years completing coursework before conducting research under a faculty advisor.

Program topics and areas of learning at CMU include:

• Language and information technology
• Language technology
• Intelligent information systems
• Artificial intelligence and innovation
• Computational data science

Applicants must submit GRE scores, official transcripts, a current resume, a statement of purpose, and three letters of recommendation. The five LTI graduate degrees let students choose a computational linguistic master's or doctoral degree aligned with their career goals.

Berkeley, CA

One of the top-ranked public institutions in the world, the University of California, Berkeley benefits from its proximity to Silicon Valley. Graduate students can pursue a doctorate in linguistics with a focus on computational linguistics through the Berkeley Linguistics Graduate Program . The program incorporates computational and experimental methods and linguistic research into the curriculum.

During their first year, graduate students complete foundational coursework in areas like phonology and cognition. Doctoral classes in experimental phonetics; psycholinguistics; and language, computation, and cognition train students for research in their focus area. After completing coursework and passing qualifying exams, doctoral students must research, write, and defend a dissertation.

Program topics and areas of learning at Berkeley include:

• Linguistics
• Computation and experimental methods

Applicants must submit letters of recommendation, transcripts, a writing sample, and admissions essays. Berkeley's linguistics graduate program admits about 10% of applicants, making it highly competitive.

Princeton, NJ

At Princeton University, graduate students can pursue a master's or doctoral degree with a focus on computational linguistics, natural language processing, or machine learning through the Department of Computer Science . Graduate students take coursework in machine learning, algorithms, and artificial intelligence, with the ability to conduct independent research in an area of interest.

The Center for Statistics and Machine Learning (CSML), established in 2014, brings together faculty and graduate students working on statistics, machine learning, and the data sciences. An interdisciplinary group, graduate students affiliated with CSML study methodological challenges in fields like computational linguistics.

Princeton also offers a graduate certificate in statistics and machine learning to complement departmental graduate programs. Students take courses in artificial intelligence, computer systems, and computational theory. The certificate helps linguistics and computer science students pursue machine learning research.

Program topics and areas of learning at Princeton include:

Ann Arbor, MI

Graduate students at the University of Michigan can pursue a computational linguistics research area through the Department of Linguistics. The department enrolls over 30 doctoral students. In the computational linguistics research area, graduate students examine the intersection of computer and human language. In addition to machine translation and speech recognition, learners study computational models of language processing.

Doctoral students complete coursework on topics like phonetics, phonology, syntax, and semantics. All doctoral students take at least two courses outside the department to promote an interdisciplinary approach. After passing qualifying exams, doctoral students complete a dissertation under the supervision of faculty.

Program topics and areas of learning at U-M include:

• Computational discovery and engineering
• Speech recognition
• Information extraction

Prospective doctoral students must submit an application, a statement of purpose, a personal statement, and official transcripts. The linguistics department also requires three letters of recommendation and a written work sample.

New York, NY

At Columbia University, master's and Ph.D. students can pursue focus areas in natural language processing and spoken language processing through the Department of Computer Science. In these areas, learners study computational approaches to text and speech analysis and generation, examining machine translation, language generation, and question answering in multiple languages.

Coursework covers topics like language modeling, information extraction, and the role of linguistic concepts in language processing. Computational linguistics students can work with the Columbia NLP Group or the Center for Computational Learning Systems, interdisciplinary research groups that let students advance their training in a focused field.

Program topics and areas of learning at Columbia include:

• Information extraction and data mining
• Information extraction, data mining

Applicants must submit research-oriented reference letters, GRE scores, and transcripts.

An Ivy League institution, Cornell University offers graduate degrees through the Linguistics Department . The department's strengths in computational linguistics, phonology, and language documentation prepare graduates for computational linguistics jobs. The Cornell Computational Linguistics Lab supports graduate students conducting theoretical and applied works on computational modeling.

The research group in neurolinguistics and psycholinguistics applies computational linguistics models to study the cognitive neuroscience of language. In addition, the Cornell Natural Language Processing Group brings together graduate students and researchers in computer science, information science, and linguistics.

Linguistics students take courses in computational linguistics, natural language learning, and neurolinguistics. The computer science department also hosts a natural language processing seminar and offers coursework in advanced language technologies, providing an interdisciplinary environment for computational linguistics students

Program topics and areas of learning at Cornell include:

• Natural language learning
• Computational phonetics

New Haven, CT

Consistently ranked as one of the top universities in the country, Yale University dates back to 1701. Graduate students interested in computational linguistics can pursue a Ph.D. in linguistics and join an interdisciplinary research group known as Computational Linguistics at Yale (CLAY). The research group brings together students and researchers studying linguistics, computer science, and cognitive science and machine learning.

In the CLAY lab, graduate learners study abstract computational models and practical applications in language technology. Current research projects at CLAY include investigations into the representation of grammatical knowledge in neural networks, the construction of practical parsing systems, and learning biases in neural network models. CLAY hosts a weekly meeting to discuss recent research papers and current work by lab members.

Program topics and areas of learning at Yale include:

• Cognitive science

Applicants must submit information on their interests in research and potential faculty advisors, including interest in CLAY. Graduates typically work in academia or the tech industry.

From its Manhattan campus, New York University offers a doctoral degree with a focus area in computational linguistics from the Department of Linguistics . Learners study the fundamentals of linguistics before pursuing advanced courses and research in their focus area. In NYU's Machine Learning for Language group, linguistics learners study natural language understanding and natural language processing.

During a machine learning for language understanding class, learners explore machine learning methods with a focus on artificial neural networks, culminating in a research paper on artificial intelligence or computational linguistics. During natural language understanding courses, students learn about artificial intelligence research and its connection to naturally occurring human language. These classes prepare doctoral students for advanced research in computational linguistics.

Program topics and areas of learning at NYU include:

• Artificial neural networks
• Computational semantics

Applicants must submit transcripts, letters of recommendation, a resume, and a writing sample. The writing sample must include a term paper, senior essay, thesis, or publication.

Chicago, IL

At the University of Chicago, prospective computational linguists can earn a graduate degree through the Department of Linguistics . One of the oldest linguistics departments in the country, UChicago offers a theory-oriented focus with an empirical interest in languages. The department encourages interdisciplinary and interdepartmental study, including work with the computer science department on computational linguistics.

Doctoral students complete foundational courses in their first two years. UChicago offers a three-course computational linguistics sequence as part of its foundational courses. The curriculum also includes classes like mathematical foundations of computational linguistics, language learnability, and introduction to artificial intelligence. After completing coursework, students must submit qualifying papers and meet the department's language requirement before advancing to the candidacy phase.

Program topics and areas of learning at UChicago include:

• Artificial intelligence
• Industrial linguistics

Applicants must submit GRE scores, transcripts, letters of recommendation, and a statement of purpose describing their research area and reason for pursuing a graduate degree.

Los Angeles, CA

At the University of California, Los Angeles, graduate students in the Department of Linguistics can earn degrees in computational linguistics, computational semantics, or computational phonology. UCLA emphasizes linguistic theory, with a focus on fieldwork and experimental study. Graduate students can take a two-term computational linguistics sequence, which incorporates lecture and lab learning on theoretical linguistics and natural language processing.

The linguistics department admits eight students per year to its doctoral program, making it highly selective. UCLA also offers a computational linguistics master's degree for students planning to earn a doctorate. All graduate students complete core classes, write a master's thesis, and take field method courses and seminars. Doctoral students complete a dissertation in their research area.

Program topics and areas of learning at UCLA include:

• Computational phonology

Applicants must submit a statement of purpose, three letters of recommendation, and transcripts. The statement of purpose should explain the applicant's background and their reasons for earning a linguistics graduate degree.

Philadelphia, PA

The University of Pennsylvania offers graduate degrees through the Department of Linguistics . The department lets students pursue interdisciplinary topics like computational linguistics, prioritizing collaboration with the computer science and psychology departments. Penn graduate students specializing in computational linguistics can join the computational linguistics research area , an interdisciplinary group of faculty and graduate students.

Penn runs the Linguistic Data Consortium , which compiles and distributes linguistic materials specifically for language engineering research. A valuable resource for computational linguists and researchers worldwide, the consortium hosts conferences and maintains a digital archive of papers in the field.

Program topics and areas of learning at Penn include:

• Language engineering

Penn typically admits students seeking a doctorate in linguistics, though it may accept candidates pursuing a computational linguistics master's degree. Applicants must submit transcripts, GRE scores, three letters of recommendation, a personal statement, and writing samples.

Seattle, WA

Founded in 1861, the University of Washington ranks as one of the top public universities in the country. The school offers a master of science in computational linguistics with on-campus and online options. The computational linguistics master's degree prepares students to pursue careers in tech or a doctorate in computational linguistics.

Master's students complete nine courses, and full-time students can earn their degree in one year. Students take four core computational linguistics classes, two linguistics classes, and three electives. Coursework incorporates hands-on work, research, and collaborative projects to provide the skills needed for computational linguistics jobs. Master's students culminate the degree with an internship or thesis.

Program topics and areas of learning at UW include:

• Grammar engineering

The program admits applicants with a background in linguistics and computer science. Coursework in programming, algorithms, linguistics, and statistics prepare undergraduates for the computational linguistics master's program.

At the University of Southern California, the Department of Linguistics enrolls 35-40 graduate students studying different areas of linguistics, including computational linguistics. During USC's five-year doctoral program, graduate students conduct research, present papers at conferences, and publish their work in journals.

Doctoral students also complete three graduate seminars in linguistics and advanced courses in their research area. After meeting coursework requirements, students must pass a qualifying examination, which includes written and oral requirements. Doctoral candidates then complete a dissertation in their research area.

The linguistics department admits 6-9 doctoral students annually. Applicants must submit a statement of purpose explaining their linguistics background and reason for pursuing a computational linguistics Ph.D. at USC. Applicants must also submit three letters of recommendation, transcripts, a resume, and a research paper.

Champaign, IL

The University of Illinois at Urbana-Champaign offers one of the only undergraduate computational linguistics degrees in the country, in addition to a computational linguistics graduate program. The schools Ph.D. in linguistics lets graduate students pursue a specialization in computational linguistics.

The program encourages students to conduct original research. The Zea Mays Center for Computational Linguistics brings together faculty and graduate students conducting computational language research in an interdisciplinary group. Through natural language processing seminars and a reading group, doctoral students strengthen their computational linguistics training.

Program topics and areas of learning at Illinois include:

Students complete coursework during their first two years, followed by qualifying exams. In the second stage of the program, doctoral students complete additional coursework and a second qualifying examination. In the final stage, students research, write, and defend a dissertation. The program admits students with a bachelor's or master's degree.

Pasadena, CA

Founded in 1891, the California Institute of Technology ranks as one of the top universities in the country. Caltech offers a research area in statistics and machine learning that allows graduate students to pursue a computational linguistics master's or doctoral degree.

Graduate students conduct research on computational linguistics, mathematical linguistics, and machine learning while earning their degree. A graduate course in mathematical and computational linguistics covers models of acquisition, learnability, and communicative efficiency.

Program topics and areas of learning at Caltech include:

• Mathematical linguistics

Prospective Caltech graduate students must submit a CV, transcripts, and three letters of recommendation. Caltech also requests a statement of purpose and GRE scores. While Caltech admits applicants from outside computer science and applied mathematics, students benefit from a background in the field.

Established in 1883, the University of Texas at Austin offers both a master's and doctoral degree with a concentration in computational linguistics through the Department of Linguistics. The interdisciplinary concentration trains students in the theory, technologies, and applications of computational linguistics. Students participate in a cross-departmental reading group on natural language learning and work with faculty in multiple departments.

During their first year, students take core linguistics classes. UT Austin offers computational linguistics classes on computational semantics, computing for text analysis, natural language learning, and applied text analysis. Graduate students also take computer science courses on topics like natural language processing and machine learning.

Program topics and areas of learning at UT Austin include:

• Applied text analysis

Prospective students must submit a statement of purpose detailing their interests in linguistics and specialization areas and their plans for graduate study. Applicants also submit three letters of recommendation and a sample research paper.

Providence, RI

An Ivy League institution located in Rhode Island, Brown University dates back to 1764. Graduate students can earn a computational linguistics degree from the Department of Cognitive, Linguistic, and Psychological Sciences . The department focuses on studies of language processing, linguistic structure, and computational processes.

Brown offers a Ph.D. in psychology, cognitive science, and linguistics, with the option to focus on computational linguistics. The program incorporates seminars, scholarly projects, and research to develop the skills necessary for computational linguistics jobs. The computational linguistics concentration lets students work with faculty from multiple departments to customize the degree around their interests.

Program topics and areas of learning at Brown include:

• Language processing

The highly selective admissions process interviews 15% of applicants and offers admission to half of the interviewees. Admitted students receive five years of tuition remission and a graduate stipend to support their studies.

San Diego, CA

At the University of California, San Diego, graduate students can earn a computational linguistics Ph.D. from the Linguistics Department . The department focuses on theoretical and experimental approaches to language, with research areas in computational linguistics, psycholinguistics, and language acquisition. Linguistics students can collaborate with faculty in the computer science, cognitive science, psychology, and anthropology departments.

Doctoral students receive theoretical and research training, taking classes in core linguistic analysis fields and learning how to conduct experimental investigations. Research training covers topics like field methods and documentation of understudied languages, computational modeling, neurolinguistics, and language acquisition. Theoretical training covers core topics like phonetics, phonology, morphology, syntax, and semantics.

With only seven required courses, doctoral students can specialize their training by pursuing courses in a focus area, such as computational linguistics. After completing coursework, students research, write, and defend their dissertation before a faculty committee.

At Duke University, graduate students can pursue a computational linguistics master's or doctoral degree through the Department of Computer Science . The inDuke program lets computational linguistics graduate students collaborate with industry partners on research projects.

Students can concentrate on areas like machine learning and natural language processing systems. Machine learning courses prepare graduate students to conduct advanced computational linguistics work. Natural language processing learners study theories of semantics and build natural language interactive systems.

Program topics and areas of learning at Duke include:

The master's program receives over 650 applicants each year, with highly competitive admissions. Duke recommends a strong background in computer science, particularly for applicants who did not major or minor in computer science during their undergraduate degree. The Ph.D. program provides full financial support for admitted students, and first-year doctoral students can take advantage of summer funding incentives to complete research.

West Lafayette, IN

Purdue University offers graduate linguistics programs for students interested in a computational linguistics master's or doctoral degree. Both graduate programs let students customize the degree around their interests, with a strong interdisciplinary focus. First-year graduate learners study the foundations of linguistics to develop their research interests. Advanced coursework focuses on the student's research area.

The School of Interdisciplinary Studies houses the linguistics department, underscoring the attention to multi-disciplinary training. The linguistics program builds strengths in linguistic and psycholinguistic development, language acquisition, and fieldwork. The curriculum blends theoretical and experimental approaches to linguistics.

Applicants must submit transcripts, an academic writing sample, three letters of recommendation, and a statement of purpose describing their academic and professional goals. Purdue also considers an applicant's background in linguistics, research, and writing.

Evanston, IL

Located on the shores of Lake Michigan, Northwestern University offers a Ph.D. in linguistics through the Department of Linguistics . The program integrates theory with advanced experimental and computational methods. Graduate students benefit from access to laboratory facilities, interdisciplinary opportunities, and full funding for their studies.

Students complete core courses before choosing from electives in areas like computational linguistics, artificial intelligence, and machine learning. The program emphasizes integrative research. Computational linguistics students can customize a research plan around their interests.

In addition to the linguistics program, graduate students can earn a master of science in artificial intelligence at Northwestern. The program incorporates technical skills to train students in machine intelligence and human-machine interactions.

Program topics and areas of learning at Northwestern include:

Applicants must submit official transcripts, GRE scores, letters of recommendation, and a CV or resume. They must also submit a writing sample and statement of purpose. The highly competitive program typically admits 4-6 students per year.

Madison, WI

The flagship campus of Wisconsin's public university system, the University of Wisconsin-Madison offers a Ph.D. in linguistics through the Language Sciences Department . Learners study topics like computational linguistics, human-computer interaction, and machine learning. Students receive foundational linguistics theory training before working with a faculty advisor to create an individualized research program.

In addition to a Ph.D. in linguistics, the department offers a Ph.D. minor in linguistics for graduate students in other departments, like computer science. The ability to minor at the graduate level lets students pursue an interdisciplinary course of study. Research groups in human-computer interaction, machine learning, and artificial intelligence bring together faculty and graduate students to solve complex interdisciplinary research problems.

• Human-computer interactions

Linguistics doctoral students generally complete the program in 5-6 years. Graduate students spend the first 3-4 years meeting coursework requirements, which includes writing two original research papers and defending them before a preliminary exam committee. The program then allows two years for dissertation research and defense.

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.

Both computational linguistics and natural language processing (NLP) apply formal training in linguistics, computer sciences, and machine learning.

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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Top Natural Language Processing Labs in Academia

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United kingdom.

Language Technologies Institute

School of computer science.

The Language Technologies Institute at Carnegie Mellon educates the leaders of tomorrow and performs groundbreaking research in the areas of:

Natural Language Processing | Computational Linguistics | Information Extraction | Summarization & Question Answering | Information Retrieval | Text Mining & Analytics | Knowledge Representation | Reasoning & Acquisition | Language Technologies for Education | Machine Learning | Machine Translation | Multimodal Computing and Interaction | Speech Processing | Spoken Interfaces & Dialogue Processing

Our unique programs offer the expertise to prepare you for a wide range of careers while staying true to the rigorous curriculum that defines computer science at CMU.

LTI faculty and students investigate more than 20 areas of language and information technologies, routinely producing breakthroughs that impact everything from education to healthcare.

The LTI offers five graduate degrees in computer science: the Ph.D. in Language and Information Technology , the research-based Master of Language Technologies , and our three professional master's programs – the Master of Science in Intelligent Information Systems , the Master of Science in Biotechnology Innovation and Computation , and the Master of Computational Data Science .

We're looking for the best students to join us.

What's New and Notable at the LTI

Diab named association of computational linguistics fellow, lti student wins nyc ai hackathon, diab receives prestigious award for development of arabic language technologies, the lti bill of rights.

Get the right information to the right people at the right time  in the right language, the right format, and the right level of detail.

The LTI makes its home inside the Hillman Center for Future Generation Technologies. We're right on campus, but an easy walk from great restaurants, coffee shops, museums and libraries.

Visitor's Guide

Natural Language Processing

University of Washington Natural Language Processing comprises diverse researchers across campus collaborating in the study of all aspects of NLP from computational, engineering, linguistic, social, statistical, and other perspectives.

Find out more:

• UW NLP talk series
• PhD programs in BHI ,  CSE , EE , and  Linguistics
• Masters degree in Computational Linguistics
• CSE professional Masters degree

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Ranking of Top Institutes for Natural Language Processing (NLP)

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The NLP Rankings site provides research rankings for faculty and academic universities, focusing on the field of Natural Language Processing (NLP). Inspired by CSRankings which gives university rankings in diverse areas, this site conducts a more thorough survey in NLP. The academic research achievement is represented by publications archived on the ACL Anthology , an open source website hosted by the Association for Computational Linguistics.

These rankings are dedicated to provide insight on research environments in different universities, and allow prospective students to acquire useful information about relevant faculty. Users can weight conferences and workshop as desired, and view the corresponding rankings for faculty and universities in NLP by the customized weights. Currently, the site gives rankings for only universities in the United States, but it will be soon expanded to universities in other countries as well.

• Contact: Jinho D. Choi .
• Developers: Chole Lee (Fall'19 ~ Spring'20).
• Ranking U.S. Universities in NLP — Part 1: Data Collection
• Ranking U.S. Universities for NLP Research Part 2: Scoring Mechanism

How to Contribute

If you find any discrepancy between your publications and what are presented on nlpankings.org , you can request for updates through the following procedures:

Updating Email Addresses

• The university ranking is determined by the domains of email addresses listed on top of every publication. If your email address is either incorrectly matched or excluded in the publication, you can make pull requests by updating the corresponding publication JSON files under the directory dat/acl_anthology/json/ .
• If you do not wish to have your email address included, leave an empty string "" instead, in which case, your contribution to the paper will not be counted towards any university.
• Only requests on email address modifications will be accepted. Please refrain from modifying any other content.

Updating University Information

• The JSON file dat/university_domain_*.json contains the name, the email domains, and the location of each university grouped by country ( us : United States).
• If your university is listed not on nlpankings.org , please update university_domain_*.json accordingly.
• If there is more than one email domain for the university, please add the addtional domains to the list. Please do not add subdomains such as cs.emory.edu .

Adding Universities Outside of U.S.

• If we wish to contribute to add universities in other countries, please create the JSON file dat/university_domain_CC.json where CC is the country code top-level domain in lowercase (e.g., for universities in South Korea, please create dat/university_domain_kr.json ).

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Why you should study Natural Language Processing at UC Santa Cruz

Our selective Natural Language Processing program is unique. Taught intensively over just 12 months and building on your background in computer science, our program provides a cost-effective way to equip you with the skills you need for a successful career in this fast-growing field. Drawing on existing expertise at UCSC, the program is delivered by a team of world-class academics from the fields of natural language processing, deep learning, linguistics, classical machine learning, and data science. Close collaboration with an advisory board of senior industry scientists from Silicon Valley R&D ensures that our program has both academic rigor and real-world context. Our expert panel of industry advisors also provide regular guest lectures, mentor teams of students for capstone projects, and attend networking and career events. Small class size enables a high level of customization for each student so that we can meet your individual development needs and career aspirations. You also benefit from being based in state-of-the-art facilities in the heart of Silicon Valley at our campus in Santa Clara.

Applying to the NLP Program at UCSC

The application period for Fall 2022 admission consideration for the NLP MS program at UCSC has closed. Applications for Fall 2023 admission consideration will open in October 2022.

Please visit our Admissions page for information about the application requirements and undergraduate preparation for the NLP MS program.*

*GRE scores are no longer required for admission consideration for the NLP MS program. You are welcome to submit your official scores, but please note the inclusion of your official GRE scores will not be a deciding factor for admission.

Want to Learn More about NLP at UCSC? Connect with our NLP Community!

We're delighted that you are interested in the NLP MS program at UCSC, and we recognize that studying for a graduate degree is a big commitment. To support you in making the career choice that is right for you, we encourage you to connect with NLP faculty, students, and staff to learn about their experiences as part of the NLP community.

Please also feel welcome to contact the NLP Program Team directly by emailing [email protected] .

Attend Office Hours with Dr. Adwait Ratnaparkhi, the Executive Director of the NLP MS Program

Meet with the nlp graduate student advisor, connect with a current student, job prospects, explore nlp ms capstone projects.

Each year, the NLP MS Program hosts a Capstone Workshop to showcase the projects NLP students complete as part of a team advised by faculty and industry mentors. To learn more about our students' projects, please click the button below.

Explore NLP Capstone Projects

Faculty Spotlight

   dr. jalal mahmud.

     "Data processing skills are essential for NLP engineers and I am excited to teach a course on data wrangling as part of the NLP MS program.”

      Dr. Jalal Mahmud is a lecturer for the Natural Language Processing MS Program and Data Science Thought Leader at IBM. Dr. Mahmud currently teaches NLP 220 at UCSC.

Click here to learn more

   Dr. Dilek Hakkani-Tür

     "Looking forward to teaching the state-of-the-art machine learning methods on natural language processing!”

     Dr. Hakkani-Tür is a Senior Principal Scientist at Amazon Alexa AI and a Distinguished Visiting Professor at UCSC.

Student Spotlight

   angela ramirez.

     "Language really is what connects us as humans. It's a beautiful thing. It's also a really rich field to study. There are so many gaps. People speak in such different ways and there's real complexity and beauty in it. So that's how I got into NLP.”

   Angela Ramirez is an NLP MS program alum.

   Adam Fidler

     "So far I like the NLP MS program. I think there will be plenty of networking opportunities, which is great because so many of our faculty members are in the industry right now."

Adam Fidler is an NLP MS program alum.

Meet our Industry Advisors

   yunyao li.

     "NLP skills are highly desired in industry, and I’m pleased to be on the Industry Advisory Board for this program.”

Yunyao Li is a Senior Research Manager with IBM Almaden Research Center.

Click here to learn more.

   Ananth Sankar

     In the NLP MS program at UCSC, Dr. Sankar has guest lectured, and as an Industry Advisor brings the perspective of someone who frequently hires engineers that do applied research and development in the areas of NLP and machine learning.

Ananth Sankar is a Principal Staff Engineer at LinkedIn.

• Leaders in areas of big data, cyber physical systems, genomics, computational media, artificial intelligence and machine learning, and human-computer interaction
• Interdisciplinary research generates cutting-edge technologies and other innovations
• Focus on engineering for social good
• World-class faculty and thriving research centers

More info regarding the Baskin School of Engineering >>

• Located in the heart of Silicon Valley, the UCSC Natural Language Processing Master's program offers unprecedented access to NLP industry companies and events.
• Home to the Games + Playable Media and Human-Computer Interaction professional Master's degree programs.
• State-of-the-Art facility with multi-disciplinary teaching and research
• Vibrant academic hub at the center of Silicon Valley

More info regarding the Silicon Valley Campus >>

• A campus with world-class facilities and one of the most visually spectacular settings in higher education.
• In 2015, Business Insider listed UCSC as one of the  "20 universities most likely to land you a job in Silicon Valley."
• Named to the 2017 US News & World report top 50 Best Global Universities.
• In 2017, UC Santa Cruz was rated  3rd in the world  for research influence by the  Times Higher Education  World University Rankings, tied with Stanford University.

More info regarding UCSC >>

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.

Fully funded four-year PhD studentships in Natural Language Processing at University of Edinburgh

October 21, 2021

UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING

Based at the University of Edinburgh: in conjunction with the School of Informatics and School of Philosophy, Psychology and Language Sciences.

Deadlines :

·       Non UK :     26 th  November 2021 ·       UK :            28 th  January 2022

Applications are now sought for the UKRI CDT in NLP’s penultimate cohort of students, which will start in September 2022.

The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation over four years. 

Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP.  

The studentships are fully funded for the four years and come with a generous allowance for travel, equipment and research costs.

The CDT brings together researchers in NLP, speech, linguistics, cognitive science, machine learning and design informatics from across the University of Edinburgh.   Students will be supervised by a world-class faculty comprising almost 60 supervisors and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs. 

The CDT involves a number of industrial partners, including Amazon, Facebook, Huawei, Microsoft, Naver, Toshiba, and the BBC.  Links also exist with the Alan Turing Institute and the Bayes Centre.

A wide range of research topics fall within the remit of the CDT:

·       Natural language processing and computational linguistics

·       Speech technology

·       Dialogue, multimodal interaction, language and vision

·       Information retrieval and visualization, computational social science

·       Computational models of human cognition and behaviour, including language and speech processing 

·       Human-Computer interaction, design informatics, assistive and educational technology 

·       Psycholinguistics, language acquisition, language evolution, language variation and change

·       Linguistic foundations of language and speech processing.

The next cohort of CDT students will start in September 2022 with applications being invited now.  Around 12 studentships are available, covering maintenance at the UKRI rate (currently £15,609 per year) plus tuition fees.  

Studentships are open to all nationalities and we are particularly keen to receive applications from women, minority groups and members of other groups that are underrepresented in technology.  Applicants in possession of other funding scholarships or industry funding are also welcome to apply – please provide details of your funding source on your application.

Applicants should have an undergraduate or master’s degree in computer science, linguistics, cognitive science, AI, or a related discipline; or have a breadth of relevant experience in industry/academia/public sector, etc.      

Further details, including the application procedure, can be found at:  https://edin.ac/cdt-in-nlp

Application Deadlines

Early application is encouraged but completed applications must be received  at the latest  by:

26 th  November 2021 (non UK applicants) or 28th January 2022 (UK applicants).

Please direct any enquiries to the CDT admissions team at:  [email protected] .

CDT in NLP Virtual Open Day

Find out more about the programme by attending the PG Virtual Open Week 9 th  November 2021, 2-3pm.  Click here to register:  Computing, Data Science & Informatics | The University of Edinburgh

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Nelson Liu's Blog

Student perspectives on applying to nlp phd programs.

This post was written by : Akari Asai , John Hewitt , Sidd Karamcheti , Kalpesh Krishna , Nelson Liu , Roma Patel , and Nicholas Tomlin .

Thanks to our amazing survey respondents : Akari Asai , Aishwarya Kamath , Sidd Karamcheti , Kalpesh Krishna , Lucy Li , Kevin Lin , Nelson Liu , Sabrina Mielke , Roma Patel , Nicholas Tomlin , Eric Wallace , and Michihiro Yasunaga .

This post offers and summarizes student advice and perspectives on the NLP PhD application process, with a focus on programs in the US. We asked twelve recently-successful NLP PhD applicants a range of questions about the application process—this post compiles the broader themes and advice that run through the majority of responses. Make sure to check out the complete set of responses ! A tarball is also available for those who cannot access Google Drive .

⚠️ Disclaimer ⚠️: While we’ve all gone through the application process and have thoughts to share, we aren’t experts or authorities on this (highly random) process. Our advice comes from our unique perspectives and backgrounds, and not everything will generalize. That said, we hope that the differences and similarities in our shared experiences will be useful to consider.

Professors have also written advice to applicants from their side of the process, see Kalpesh Krishna’s compilation of graduate school application advice .

Table of Contents

Pre-application, statement of purpose, letters of recommendation, publications, transcripts / grades, standardized exams: gre / toefl, interviews / post-application calls, deciding where to go, misc. topics, in conclusion.

Deciding to apply at all is not an easy choice, and several respondents took additional time, either in school or in industry, to explore new fields and become more certain that pursuing a PhD was the right decision for them. Choosing where to apply is also an involved process, and involves trade-offs between factors like research area fit, location, and (perceived) selectivity. This section explores this preliminary part of the application process, along with useful insights from applicants on different aspects of this decision.

A lot of the perspectives in this post are aimed towards people already seriously considering a PhD—for instance, seniors or MS students. If you are a student considering a PhD, but still have a significant amount of time before you apply, John Hewitt’s blog post contains useful insights and advice on how to make the most of your time in school. In addition, Kalpesh Krishna’s extensive compilation of application advice might yield things to keep in mind through the years.

Why apply now?

For many of the respondents, starting a PhD was the natural “next step”—they were in the final year of their undergraduate or masters degrees, and had spent enough time doing research to realize that a PhD was worth the opportunity cost to them.

While I did not have any *ACL papers while applying...My goal was to get into a good PhD program and start doing research full-time (which is why I was applying to a PhD program in the first place) rather than get into the very best PhD program. – Kalpesh Krishna

Waiting to apply also has clear benefits—many respondents felt that they would be stronger applicants after an additional year of research experience (and the associated publications and stronger letters of recommendation that might come with it).

“The year away from academia gave me the clarity on how much I really wanted to do a PhD and how much I love academic life. In this year I used my free time to explore interesting research directions and collaborated with friends. It made me realise that I enjoy research and to be able to do it for a living would be just perfect.” – Aishwarya Kamath

“I was also unsure at that time what kinds of directions I wanted to go in or if I even wanted to commit so many years of my life to additional school...By the time fall 2018 came around, I’d done a full year of thinking and growing my research skills, so I felt a lot better about diving into the process.” – Lucy Li

Several people found value in waiting because it gave them the time to reflect on their next steps. For instance, Lucy and Aishwarya used the time to further develop their research interests and think about what areas were exciting to them. In particular, Aishwarya spent a year in industry, which made her realize what she was missing an academic setting and drove her to apply and return.

On the other hand, several also offered caution about waiting with the sole intention of improving your profile. As PhD applications get more and more competitive each year, more papers or experience doesn’t necessarily mean a stronger application since things are inherently relative. Several agreed that having publications at top conferences is not a necessary component of a strong application, especially if one has relatively limited research experience (e.g., applicants from undergrad) or has strong recommendation letters. A recent blog post about the machine learning PhD application process investigates admission statistics at one of the top schools (Fall 2018), and shows admission is not determined solely on publication records, but depends on the other factors, especially applicants’ background and letters of recommendations.

For instance, Kalpesh and Akari considered waiting a year since they did not have any top-tier NLP publications at the time, noting that:

• Things get more and more competitive each year, so more papers doesn’t necessarily mean a stronger application since things are inherently relative.
• Applicants with master's degrees are expected to have more publications and experience than undergraduate applicants.
• There is a large amount of uncertainty involved in research / writing papers, so things are not always going to pan out for reasons out of your control.
• They thought that they were still reasonably strong applicants for many of the places they were applying to.

Kevin and Akari also mention that, if you have the resources, you can apply multiple times.

If what you really want to do is to immediately get into a grad school and continue doing work that you are excited about, you should apply. – Roma Patel

Choosing where to apply

When choosing where to apply, the majority of respondents focused on a few factors:

• Overwhelmingly, the strongest factor for everyone was faculty : finding schools with professors that you’d want to work with, and with a strong presence in allied fields. Several mentioned applying to places only if there were 2 or more relevant faculty.
• Location was also a key factor for many: finding schools in places that you think you’d be happy living in for 5+ years.
• Lastly, many also considered proximity to industry connections / possible external collaborators .

Some also took the relative prestige of a school into account, with the thinking that prestigious schools attract strong peers, which means that you can learn more and work with amazing people.

There’s also a case to be made for applying to a mixture of (1) programs that you’re relatively confident you can be admitted to and (2) “top choice” programs that might have a bit more randomness in the admissions process (of course, all the schools you apply to should be places you’d be happy going to). However, it’s easy to be a bit too conservative when choosing where to apply—remember that you only really need 1 offer. The majority of respondents applied to between 8 and 13 schools, though almost everyone was happy with the number of applications they submitted (Kevin, who applied to 4, thought it would have been helpful to apply to more).

NLP applicants in particular are lucky—there are amazing faculty scattered around the world in a variety of different environments. Start with a large list before filtering down, and focus on finding the right fit for you personally.

Talking to Faculty Beforehand?

I did not email faculty beforehand - I don’t think this helps (and in the case of a poorly crafted email, could actually hurt!). – Sidd Karamcheti

The majority of students did not email faculty before applying. Some faculty ask students to reach out—this will usually be explicitly mentioned on their webpage. In the absence of such a notice, a reasonable policy is to not send an email.

But that said, if you are in the vicinity of a school or doing an academic visit -- feel free to reach out to the faculty there and ask if they have a half-hour slot to meet! – Roma Patel

I emailed one prospective advisor and asked to meet at a conference. In general, I think this is a good strategy, especially if you have research-related things to talk about with them. (Which hopefully you will, if they’re a good advisor fit!) – Nicholas Tomlin

Several respondents were fortunate to meet potential future advisors at workshops or conferences / if they happened to be in the area, and found them to be quite receptive to short research meetings. It’s good to go into these meetings with a sense of (1) what you’d like to get out of it, and how to use this meeting effectively, (2) an awareness of their recent work, (3) a mental list of questions that you think have informative or interesting answers.

...one of my undergrad advisors emailed a couple prospective grad advisors on my behalf, and asked them to look out for my application. I think this was particularly helpful and is maybe something worth mentioning to your undergraduate advisor. – Nicholas Tomlin

It is appropriate to selectively ignore advice about cold-emailing— Prof. Yonatan Bisk has a great guide that walks through the why, when, and how .

Back to the top.

The statement of purpose is an opportunity for you to convey what you’ve worked on and what you’re interested in. Above all, make sure the statement is genuine and uniquely you. The “accept/reject” dichotomy of applications might make this process seem like a game—leading many to believe that it’s better to win the game (that is, be accepted) than to lose. While it’s tempting to shape each application to say what you think faculty might want to hear, being yourself will lead to the best outcome in the end. Remember that programs and students are both looking for the right fit—the statement is a fantastic opportunity for both sides to assess this.

If your statement is genuine and makes clear why you want a PhD, it will resonate with the people you want it to resonate with. – Sabrina Mielke

Timeline: When to Start and Finish Writing

With respect to starting writing, it is sometimes good to leave it late enough to wrap up any ongoing research projects at the end of the summer so you can write concrete things about them. For finishing writing, it’s good to have a near-ready draft at least a month before. – Roma Patel

Try to set aside a fixed period of time to work on your statement. While starting earlier rather than later is usually better, try to start writing a draft once you think your current projects and interests are concrete enough to write something substantive. Strive to have a preliminary draft that you’re happy with at least a month before the deadline. You can then send this to your advisors for feedback; continue editing and iterating until the deadline and/or you’re happy with how things look.

Structuring a Statement of Purpose

The goal of the statement is to talk about your past (research) experience, and how that has prepared you for a career in research (why you’re qualified for grad school). – Sidd Karamcheti

Your statement of purpose should uniquely describe your research experience and elaborate on the process you went through as you undertook your first few research projects. Give enough detail about your past work to allow them to assess the value of the work and also to concretely show that you knew what you were doing at every step of the process. Then fold this into your research as a whole. Try to leverage insights from both the actual work as well the experience of doing research, to formulate how you would undertake future projects during your graduate school career.

Many professors do tell you what they’re looking for in a SoP (JHU CLSP for example has hints at https://www.clsp.jhu.edu/apply-for-phd/phd-admissions-faq/ ), so do use that resource. – Sabrina Mielke

Tailoring Each Statement for Specific Universities

I only tweaked the final paragraph. In this paragraph, I specifically mentioned 2--4 faculty that I wanted to work with and provided a one sentence rationale. – Eric Wallace

Our survey respondents were quite divided on this question. A few respondents significantly tweaked their statements for each university to reflect the subset of their interests relevant to the prospective advisor’s research. However most respondents kept 80-90% of their statement identical and only modified the last 1-2 paragraphs with university specific information - such as the names of the professor they were interested in working with. Most agreed that it is good to have at least some university-specific information to form a connection between your own research goals and a prospective advisor’s research directions.

It is good to have concrete reasons laid out in your statement as to why you want to go to this school and work with these faculty on interesting problems. So definitely tweak the section of your statement that stresses on this. – Roma Patel

Getting Feedback on Your Statement

Your recommenders will get a better sense of your research interests so it can help them write your recommendation and they have also been through similar processes. – Kevin Lin

It is good to have a near-complete draft of your statement ready in time to send to your recommenders before they begin to write your letter of recommendation. There are multiple benefits to this. Reading your statement will help them better understand your research interests, which will not only allow them to concretely write things about you in their letter, but might also bring up useful pieces of advice from them based on what they know of the people working in that research area. They will also usually give you feedback on the overall statement—they have possibly read countless statements over the course of their career and will be able to fairly judge and evaluate this in context. Your research advisors and recommenders are likely both extremely knowledgeable and also have your best interests at heart, so remember to ask for feedback and advice on your application!

Using this as a Learning Opportunity

In my statement, I mostly talked about my past experiences and how they feed into my current research interests. I tried to paint a picture that enables the reader to better understand how I reached / why I do the research I do. – Nelson Liu

Write out your journey as a researcher from the beginning to the present. This will convey important information about you and your research, which can be illuminating for both your reader and for yourself. Chances are that you will write dozens of similar statements in the future, whether they are research statements for fellowships, project proposals, or grant applications. Use this as a learning experience! Writing your statement of purpose is not only good practice for the future, but also a rare invitation to reflect upon your interests and motivations.

Letters of recommendation are often cited as the most important part of a PhD application. In our survey, every respondent marked letters as either the most or second-most important component. Given that the admissions committee is optimizing to admit candidates with a high likelihood of reliably producing excellent research, a letter from a fellow academic that effectively claims you’ve been able to do so is a strong signal that you’re a good candidate.

What to look for when choosing letter writers

Your letter writers should be people who know you well enough to speak about your skills and your strengths as a PhD candidate ... people you have worked with who are doing relevant research in the field and people you have genuinely been advisors to you… – Roma Patel

It can be helpful to view letter writers as your primary advocates in the admissions process. They want their excellent undergraduate students or research assistants to succeed, and they’re singing your praises in order to argue for your spot in graduate school. From this view, it may be clear that they should know you, your strengths, and your goals. Of course, some of your letter writers will know you better than others, but each should be able to at least advocate for your excellence in how you worked or interacted with them.

There’s often a tradeoff between (1) how well you know the letter writer, (2) how cool the work you did with them was, and (3) how well-known they are. As a first approximation, attempt to have all 3 letter writers know you through some kind of research collaboration. Simply doing well in their class, or TAing for them does not necessarily make for a strong letter. On the other hand, an industry researcher who can vouch for your research ability may be able to make a stronger statement. This brings us to (3) how well known the letter-writer is. Perhaps unfortunately, letters from well-known members of the field are (very) highly regarded. This may be due to fame bias—the professors on the application committee can rest assured that they know so-and-so from X university consistently recommends only excellent students. As suggested at the beginning of this paragraph, this will play some role in the tradeoff, but keep in mind that a famous professor who doesn’t really know you won’t write a strong letter.

Each of the components mentioned above—personal knowledge of you and your work, successful research and fame of the writer were mentioned by our respondents.

I chose professors with whom I had completed somewhat successful research, and who were likely to be known by my prospective advisors. For better or worse (probably worse), connections between letter writers and prospective advisors seem to matter a lot. – Nicholas Tomlin

When to start looking for recommenders

People get started in research at different times, but by the time of application, you need three people who can advocate for your spot in graduate school (though again, not all need to be equally strong or know you equally well). When should you start building these relationships? The easy answer is “as early as possible”. Research takes a long time, as does getting settled in a field and starting to make real progress. This creates a definite bias towards those who start research earlier and collaborate widely (3 professors means a lot of connections to make). However, everyone’s research story looks different, and no student should think it “too late” to go for a PhD (though a master’s and/or further years of research experience may be necessary.)

To back this up, note the wide range of times that our respondents started working with the people who would end up being their LoR writers.

Note that this histogram includes one data point for each letter writer for each respondent. (Not everyone mentioned all three writers, and one mentioned four.) I counted “summer before 3rd year” as “2nd year.” That’s a lot of letter writers from the third and fourth (!) years. Many respondents who met their letter writers after their third year did indicate that it would have been better to start earlier, but the data somewhat makes sense—as you progress through your studies, you gain more research experience.

Asking for specifics in your letter, and getting them submitted

Recall that your letter writers are your advocates—you should feel empowered to bring up all the awesome things that you did with them, and ask (but not demand) that they mention specific things. These requests may be to tailor their letters to your statement of purpose. Think that your efforts in conducting replicable science in a world of AI hype are awesome? Your letter writer may agree, but likely wouldn’t think to mention it if you don’t remind them.

I made sure to send a reminder email 2 weeks, then 1 week, then a few days before applications were due. – Nelson Liu

Likewise, remember that they’re human and busy, and very well may forget your letter if you don’t send them a few reminders. PhD applications tend to have lenient letter of recommendation deadlines but it’s better to keep on top of them with tastefully-spaced reminder emails—better to not test the waters in this context.

I think that having a published conference paper greatly increases your chances, but I think that papers are merely a signal for something more important: can you complete the full research process, from idea inception to experiment execution to writing things up? – Nelson Liu

Most respondents felt that publications are an important part of a strong application, but are not necessary if you have stellar recommendation letters talking about your research aptitude. Admission into PhD programs in computer science (especially at top schools) is quite competitive, and many candidates have publications, especially candidates applying after year-long research positions such as AI residency programs.

Publications are just tangible evidence - if you can show other evidence that you are able to do research, that you learned something, that you have skills/conclusions that you’ve taken away from the experience, then you should be fine. – Sidd Karamcheti

Publications are a good way to show concrete research output. This acts like “hard evidence” of research aptitude, which is the primary criterion used to judge PhD applicants. Alternative ways to show concrete research output could be excellent research code releases or insightful blog posts.

Almost all survey respondents thought that grades and GPA scores play only a minor role in NLP PhD admissions. It is wise to not stress too much about improving your GPA, especially if compromises the time spent doing research. Things might be different in more theoretical fields though, where coursework might be closer to research.

Take an intro to NLP course! Take machine learning or a specific linguistics course or anything else that clearly shows that you have studied the topics you are excited about in depth. – Roma Patel

Interesting classes off the beaten path may let you stand out from the crowd. – Sabrina Mielke

The choice of coursework typically acts like a skillset evaluation during PhD admissions, checking whether candidates are familiar with the fundamental techniques required to conduct their research. Coursework can also help present a coherent academic history when combined with the statement of purpose. Some courses might help an applicant stand out from the crowd, especially if they’re uniquely relevant or off the beaten path.

Sometimes, the exact preparation matters less than evidence that you’re capable of learning important background material. E.g., despite me not having strong probability/stats background, a few professors said they were impressed by my (completely irrelevant) pure math background. – Nicholas Tomlin

While coursework does not play a major role in admission decisions, many respondents mentioned that courses are a great way to learn the fundamentals and get interested in a particular field, often acting like a precursor to research.

I get the sense that the GRE doesn’t really matter unless you do abysmally. – Nelson Liu

Nearly everyone agreed that scores from required standardized tests are not deal-breaking as long as you meet a minimum threshold. Having a suspiciously low score could raise questions, but barring failing the exam, this should not significantly impact your entire application. That said, this is a required checkpoint on your application, so keep aside time to get this done correctly.

There is no glory or shame in taking too much or too little time, so it is better to not compare to others and keep aside the right (and possibly minimal) amount of time you think you need to prepare. – Roma Patel

Try to give yourself at least 1-2 weeks of study time before the actual test. Don’t consider the amount of time you see others spending on this — assess yourself and allocate larger amounts of time to topics that you are uncertain about and think could use the extra effort. Remember to review all the topics you need to, take a few practice tests, and then just take the exam and don’t stress about the score.

It is usually not worth the extra time, effort, cost (or effect) to redo the exam. So prepare well once, take the exam, and don’t stress about the score once you are done with it. For what it’s worth, future years will likely see this disregard and ambivalence towards scores on tests heightened — lots of schools have already removed the GRE requirement, while others have definite plans of doing so in the coming years.

In general, international students must submit their TOEFL (or IELTS) scores to demonstrate competency in the English language — however for some schools, international students who have received degrees in US schools or received their instruction in English do not need to submit TOEFL scores. Unlike in GRE, applicants MUST score higher than the minimum requirements if universities sets minimum scores. The minimum requirements vary from program to program. For example, the Cornell CS PhD program sets the minimum scores for each section (Listening 15, Writing 20, Reading 20, Speaking 22), while the MIT EECS PhD set the total minimum scores to 100. Make sure that you meet TOEFL scores before the application deadline. Unfortunately, the applicants whose TOEFL scores lower than the minimum are likely to be “desk-rejected”.

Interviews in USA are less formal - more general discussions about research interests. Interviews for Europe in my experience were more in depth, as they expect you to already have knowledge of your field (since you can only apply after a Masters), have a research plan and expect you to have already surveyed literature in your chosen field of interest. – Aishwarya Kamath

The interviews and visit days will differ significantly over the range of schools you’re considering—both in their intended purpose and in the amount of information you can glean about the school and faculty from this one interaction. Some schools do pre-acceptance visit days, with offers conditioned on the interviews and ensuing discussions. Others do virtual interviews over the phone or video calls. And of course, some schools choose not to conduct interviews.

While each interview experience is largely dependent on the candidate in question, most of our survey respondents agreed that these conversations follow the same general pattern.

The general format was like: “Tell me about a research project you worked on (pick one that is most exciting and introduce)”. The professor would ask some questions, like “why did you consider this model / run this experiment?”, “what is the conclusion?”, “what did you learn through this project?” “What is your research interest?”, “What are you interested in doing for your PhD (and your career)?” -- it’s good to think in both short term and long term “Do you have any questions?” -- you can ask any questions about the lab, like the culture, research goals, how advising/meeting works. – Michi Yasunaga

This is mostly a means of trying to get a sense of what you are like as a person and what your research interests are, to assess both compatibility and mutual interests. Your interviewers will generally ask you to talk about the research you have done — and will interrupt with questions about things that they are interested to hear more about. Overall, this is less of an assessment of your knowledge, rather than them getting insights into how you solve problems and talk about research.

I didn’t enjoy the whiteboard interview. – Nicholas Tomlin

This sometimes happens. If professors want to assess a specific component of your application, or want to know the extent of your knowledge about a certain topic, they will ask you technical questions that can range from explaining or solving an algorithm, writing out equations or explaining computational and implementation-specific aspects of things you have done. Most of our survey applicants however, did not have to go through this and their interviews largely consisted of general research conversations.

You should definitely know your own work inside-out, but don’t stress about having to know every intricate detail about every subfield in NLP. – Roma Patel

While it is not important (or even possible) to know everything little thing about every research area in NLP, you should be aware of work being done in areas related to you. Most importantly, if you have written about something in your statement, you should be able to confidently speak about it and answer any questions that they throw at you. Take time to look into every detail and ensure that you know the fundamentals of your work before your interview.

Remember that this is a two way street—while they’re assessing whether you’d be a good fit for their program, you should be probing whether this place / professor is a good match for you. – Nelson Liu

There is usually a part of the interview where the interviewer steps back and asks you to ask questions — use this time to probe at any uncertainties or lingering questions that you have. If you have questions about their previous work, thoughts about future possibilities, or even just general questions about the program or the department, use this time to clear any doubts and get all the answers you will need to make a decision.

if you don’t know something, it is okay to say that you don’t --- ask questions that help you understand it more and treat it as a learning experience. – Roma Patel

The only thing I will tell you not to do in an interview: pretend. Professors are good at spotting that kind of thing and they will strongly judge you for it. Just be honest and genuine. You are starting your PhD. You don’t need to know things -- just be willing to grow. – Sabrina Mielke

Also, don’t worry if you do not know everything the interviewers ask. Just try to be as honest and genuine as you can, and show that you are willing to learn and grow, instead of pretending to know the topics.

I think the interviews as an initial conversation really affected where I seriously considered—the places with interviews that I thought were more fair / reasonable gained legitimacy. In the best case, it was basically a research conversation with a senior researcher, and a great opportunity to get feedback / hear what they think about the field. Overall, I thought they were quite valuable, and I wish that I had treated them less as assessments and more as opportunities. – Nelson Liu

Make the most of your interviews! All applicants agreed that overall, the interviews were friendly and engaging experiences. Think of this as an opportunity to speak about and answer questions about your work and to have a mutually engaging research conversation.

One useful piece of advice from one of my undergrad advisors was to, “Talk about your research ideas! Remember that what most faculty really want is to be able to discuss the research that is important to them — and if you can do this and make exciting progress through these discussions, you will both mutually have a productive and happy career together.” – Roma Patel

If you’re fortunate to be considering multiple options, congratulations! It is a hard problem, but a good one to have—be aware of your privilege. The choice between graduate programs is an intensely personal one, and there are a variety of academic and non-academic factors to consider, all of which will influence your health, happiness, and productivity.

Something that people do not always remember when making a decision is that your advisor is possibly someone you will be talking to for upto 3 hours every week for nearly 6 years of your life. It is good to rethink whether or not you will be happy doing this with the faculty in question, if the two of you see eye-to-eye, can comfortably talk about both research-things and also life-things when they come up, and that they will encourage and help guide you in everything you need to do the research that is important to you during your PhD. – Roma Patel

In general, most respondents agreed that the most important factor is your primary advisor—who will you be working with during your PhD? Do you have mutual research interests? Are your communication and working styles compatible? Would you be comfortable talking to them about your struggles, both academic and non-academic? Do you have much to learn from them and their group? Do you feel supported by them? While it is hard to assess these deep questions before spending time to work with them, conversations and interactions during visit days will help you get a sense of whether things feel right. Trust your instinct—if things feel odd or unnatural, even during these initial conversations, you have plenty of reason to reconsider and be hesitant.

As an undergrad at a school with a large NLP community, I really benefited from having senior researchers around (e.g., grad students and postdocs)---I have so much to learn from them! I felt like I wanted to keep having such an environment in graduate school, which actually ended up being one of the defining factors in my final choice. – Nelson Liu

Many students also took note of the NLP community at every school they were considering. For instance, some prefer larger groups with many senior students and postdocs, while others prefer smaller, more-intimate groups. There are benefits and drawbacks to both sorts of research environments, and it ultimately boils down to personal preference and taste. It’s important that you feel like you have enough people around to talk about research and life—while your advisor is an important figure in the PhD, you will spend the majority of your time talking to and working alongside fellow students. Make sure that these are people that you’d love to be around for the next stage of your research career.

Sure, you’re picking a place to do research for the next 5+ years of your life, but you also need to be happy / have a life outside of research...I went climbing during a lot of my visits, mostly to assess convenience. – Nelson Liu

Another important factor to consider is the location. Several expressed weather / culture preference (mostly on the east-coast-vs-west-coast divide). Many also wanted to be in a place that was affordable for students and conveniently located to their favorite hobbies or recreational activities. While research fit is certainly important, you won’t be productive if you’re miserable—put your happiness and your health first, and make sure that you’ll be happy as both a student on-campus and as a resident of the area.

Prestigious schools attract strong peers, which means you can learn more and collaborate with amazing people. – Eric Wallace

Several also considered the relative “ranking” of a university or program (though this is almost impossible to objectively evaluate without implicitly considering the other factors). While rankings can tell part of the story, they’re not substitute for your own feelings and intuitions about where you belong.

At some schools, it was very clear who my advisors would be, while at others, it wouldn’t be decided until I’d enrolled. I preferred the former scenario since it involved less uncertainty. – Lucy Li

It’s also useful to consider the program’s requirements and logistics around advising. Are you guaranteed to be able to work with the advisor(s) you are interested in? Does the department have extensive qualification exams or requirements that might be hindrances to your productivity? Will you have to worry about funding?

Personal feelings actually do matter. If you feel (even slightly) uncomfortable, these negative feelings will grow during the five years. – Akari Asai

Once you have done an extensive comparison on all parameters (professional and personal), you might be stuck between 2-3 very good options. Try reweighting the parameters and see if the balance shifts towards one end. If you are still confused, don’t worry :) If it’s so confusing, both places are surely very good. You will need to work very hard wherever you go, and you won’t lose much choosing one over the other. Go with your heart. – Kalpesh Krishna

When it comes to the final decision, everyone agrees to go with your heart and feelings of what seems right to you. We’re all logical and analytical people (perhaps to a fault), but if you can’t make up your mind about where to go / are stuck between several options, pick the one that you feel the best about inside. One way to discern this: Suppose you’re picking between two places (this strategy generalizes to N). Take a coin, and assign one place to heads and another to tails. Tell yourself that the result of the coin flip will be where you end up going. Flip the coin, and observe the result. Are you relieved? Would you have preferred the other side? The answer to these questions might help you better understand how you really feel about the decision.

Whatever you do end up deciding, though, don’t regret it—the decision is done now, and you just have to put in the work to ensure that it is a good one. – Nelson Liu

Making the most of visit days

I didn’t end up going to most visit days -- which is not something that you should do. Go to every visit day! Talk to the other students visiting, the other students currently pursuing PhDs there and to the faculty there. Keep a list of standard questions about schools (requirements, professors, exams, time taken) and make a note of these for every school so that you have an easy way to compare at decision-making time. – Roma Patel

Many of our survey respondents recommend making the most of the visit days. Treasure this priceless opportunity to talk to professors (both in and outside of your field), meet PhD students, and get to know the other students in your cohort. As you continue your academic career, you’ll be seeing all of these people around in the future—get to know them now!

Talk to students most of all -- disturb them when they’re working to see what it’s like in the lab! – Sabrina Mielke

Before each visit, it’s useful to think a bit about what you’d like to get out of it. This might result in a list of questions you’d like to answer, or people that you’d like to talk to. Don’t be afraid to contact PhD students in the department and ask to meet; the majority are happy to do so, and would love to give you advice, hear about what you’re working on, and talk about their research. Talking to students is of the utmost importance; they will tell you what it’s really like in the department, and it’s useful for getting a sense of the overall department culture and graduate student community.

My advisor, in her infinite wisdom, gave me a useful piece of insight that had not struck me before. "What most people don't realise, is that the people that you are meeting and talking to over these visits will likely be in your life, for the rest of your life. Go to as many visits and talk to as many prospective students as you can — some of your closest friends and advisors will come out of these interactions." – Roma Patel

Residency Programs as Precursors to your PhD

I see a couple benefits of working in AI residency which I did at AI2. 1) if you aren’t sure if you want to do a PhD, this is a pretty good way to find out, and after the residency you will be in a reasonable position to pursue both industrial and PhD positions. 2) You will be exposed to a new set of people, and it is helpful to learn from different ways of doing research 3) I personally changed my research direction towards more NLP and this was a great way to explore different research topics and build up the skills I needed to pursue those topics. – Kevin Lin

Be really really clear why you’re doing the residency - the reason to do the residency/work is to do something you could not otherwise do at grad school/if you’re not sure about grad school. – Sidd Karamcheti

It’s really important to consider why you want to do a residency program. As our survey respondents mentioned, there are a few different paths that lead to residencies—foremost among them is if you’re not too sure about wanting to do a PhD, and you want some more research experience (working with a couple of different mentors with possibly different areas/interests than what you were exposed to as an undergraduate) before making a final decision.

Another reason a residency program is a good idea is if you’re sure about doing a PhD, but had limited exposure to different areas as an undergraduate. Especially if you’re considering PhD programs where you’re paired with an advisor/placed in a specific area outright, having a year to explore a bunch of different areas and work with different mentors with different styles will let you make a more informed decision. It’s totally possible that the residency program will introduce you to areas you would never have otherwise considered!

That being said, it’s worth noting that not all residency opportunities are created equal—several different companies are just in their first or second year of offering their residency programs, meaning that they’re subject to growing pains—without structured onboarding/tutorials you might spend a lot of time trying to figure out how to use company infrastructure, or you might spend a lot of time trying to figure out what different folks at the company are working on, and how research works in industry.

More importantly, you need to make sure your residency mentors are committed to the same goals that you are—a mismatch in expectations between you and your residency mentors is going to significantly sour your experience! If you want to explore a bunch of different sub-areas of your chosen research area, make sure your mentor is on board to try a few different projects over the course of the year! If you want to instead work on more long-term projects/existing initiatives at the company, make sure that your host is willing to connect you with these existing teams, and that there’s some structure in place that will let you (1) learn, and (2) contribute.

Finally, don’t feel like you need to do a residency to get the industry experience, or to explore different research areas. There is definitely a large amount of time you can spend exploring different areas in grad school, and you’ll have multiple summers to do internships where you’ll possibly get to work on projects very different from your core research agenda.

FWIW, you will likely intern at a lot of the places during the course of your PhD and will have a similar experience, so if the only reason you are considering a residency is because you think that is an experience you will never get at a later time --- this is likely not true. – Roma Patel

When submitting my application, I was pretty sure that I would defer for a year if I got an offer---there’s no rush, and the extra year might give me some interesting perspective. – Nelson Liu

If you’ve read this far, we hope that this discussion was useful. The admissions process is inherently stochastic, and there’s much that you can’t control—relax, have confidence in yourself, and goodluck!

Another good advice I received from my friend was “Don’t reject (by?) yourself”. I remember how uneasy and stressful I felt at the time of application, as I did not have strong publication records, and came from non top undergraduate schools in the US. Sometimes people value your unique back-ground, experience in other fields or find really positive signals in the letters of recommendation. Don’t hesitate to apply for good schools, because “I think I’m not good enough”. – Akari Asai

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PhD Students

• M. Omer Gul
• 9 receive 2023 Bowers CIS-LinkedIn grants
• Cornell natural language processing scholars win Best Paper at top conference
• Inaugural Grants Announced from Strategic Partnership with LinkedIn
• Joachims, Myers receive prestigious named professorships
• Trummer wins runner-up award at SIGMOD

Please visit the Cornell NLP Group website for more information.

• Program Overview

PROGRAM OVERVIEW

Natural Language Processing (NLP) combines the academic disciplines of computer science, linguistics, and artificial intelligence to develop computer programs with the ability to understand text and spoken words.

PEOPLE OF NLP

Our program is delivered by a team of world-class academics from the fields of natural language processing, deep learning, linguistics, classical machine learning, and data science. Many are at the forefront of their fields, producing world-leading research and contributing to advances within their area of expertise.

Learn about our NLP Faculty, Industry Advisory Board, Program Team, Current Students, and Alumni.

Questions about our program? Get in touch with us via our contact page here:

Why you should study Natural Language Processing at UC Santa Cruz

Our selective Natural Language Processing program is unique. Taught intensively over 15 to 18 months and building on your background in computer science, our program equips you with the skills needed for a successful career in this fast-growing field. Drawing on existing expertise at UCSC, the program is delivered by a team of world-class academics from the fields of natural language processing, deep learning, linguistics, classical machine learning, and data science.

Close collaboration with an advisory board of senior industry scientists from Silicon Valley R&D ensures that our program has both academic rigor and real-world context. Our expert panel of industry advisors also provide regular guest lectures, mentor teams of students for capstone projects, and attend networking and career events. Small class size enables a high level of customization for each student so that we can meet your individual development needs and career aspirations. You also benefit from being based in state-of-the-art facilities in the heart of Silicon Valley at our campus in Santa Clara.

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Best Global Universities for Artificial Intelligence

Artificial intelligence is a subcategory of computer science focusing on research to create machines that attempt to problem-solve and analyze contradictory or ambiguous information. Topics covered in this category include artificial intelligence technologies such as expert systems, fuzzy systems, natural language processing, speech recognition, pattern recognition, computer vision, decision-support systems, knowledge bases and neural networks. These are the world's top universities for artificial intelligence. Read the methodology »

To unlock more data and access tools to help you get into your dream school, sign up for the  U.S. News College Compass !

Here are the best global universities for artificial intelligence

Tsinghua university, nanyang technological university, chinese university of hong kong, university of technology sydney, national university of singapore, harbin institute of technology, university of adelaide, university of electronic science & technology of china, peking university, university of chinese academy of sciences, cas.

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• # 1 in Best Universities for Artificial Intelligence
• # 23 in Best Global Universities

Tsinghua University, located in northwest Beijing, China, is a public institution that traces its roots back to 1911... Read More

• # 2 in Best Universities for Artificial Intelligence
• # 30 in Best Global Universities
• # 3 in Best Universities for Artificial Intelligence  (tie)
• # 53 in Best Global Universities
• # 112 in Best Global Universities  (tie)
• # 5 in Best Universities for Artificial Intelligence
• # 26 in Best Global Universities
• # 6 in Best Universities for Artificial Intelligence
• # 196 in Best Global Universities  (tie)
• # 7 in Best Universities for Artificial Intelligence
• # 74 in Best Global Universities  (tie)
• # 8 in Best Universities for Artificial Intelligence
• # 231 in Best Global Universities  (tie)
• # 9 in Best Universities for Artificial Intelligence
• # 39 in Best Global Universities  (tie)

Peking University is a public institution that was founded in 1898, though it wasn't known by its current name until... Read More

• # 10 in Best Universities for Artificial Intelligence

CSRankings: Computer Science Rankings

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This ranking is designed to identify institutions and faculty actively engaged in research across a number of areas of computer science, based on the number of publications by faculty that have appeared at the most selective conferences in each area of computer science (see the FAQ for more details).

We gratefully acknowledge the generous support of our sponsors, including Stony Brook University . Sponsor CSrankings

Prominent mentions of CSrankings: CMU ( 1 , 2 ) | Edinburgh | Michigan | Rutgers | Technion | Yann LeCun | John Regehr | Charles Sutton

CSRankings by Emery Berger is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License . Based on a work at https://github.com/emeryberger/CSrankings . Follow @csrankings for updates. Copyright 2017-2023 © Emery Berger

UC Santa Barbara NLP Group Recruiting Ph.D. Students for 2022!

Written by william yang wang, multiple ph.d. positions in nlp/ai/ml at ucsb.

Figure 1 — Come join us at the UCSB NLP Group as a new Ph.D. student!

The Computer Science Department at University of California, Santa Barbara invites applications for multiple PhD student positions in Natural Language Processing, Artificial Intelligence, and Machine Learning. UCSB Natural Language Processing Group includes Profs. Shiyu Chang , Lei Li , Simon Todd , William Wang , and Xifeng Yan .

We are looking for students who are:

• Self-motivated
• Passionate about advancing AI
• Proficienct programmers and problem solvers
• Mathematically mature and proficient in ML-relevant statistical methods
• Familiar with deep learning frameworks such as TensorFlow, PyTorch, or MXNet.

UC Santa Barbara is a Top 5 U.S. Public Research University , and the UCSB NLP Group ranked #4 in the most productive NLP groups in 2018-2021 in America . Recent UCSB CS and NLP PhD graduates and postdocs have taken tenure-track faculty positions at prestigious schools such as UIUC, UCSD, Waterloo, UChicago, UT Austin, UMich, Dartmouth, Purdue, Virginia Tech, OSU, UCSC, Northeastern, and Rochester. Our PhD students were placed into top industry internship positions during Summer, including Facebook AI Research, Google AI, Microsoft Research, Salesforce, Amazon etc. UCSB boasts to be one of the most beautiful campuses in the world with perfect weather year round.

UCSB’s international scholar office will provide support for visa, and we will provide top academic PhD student salary, health insurance, benefits, and travel budget for conferences. Qualified candidates may directly apply to UCSB’s CS PhD program and indicate faculty of interest in the personal statement and the application system.

PLEASE NOTE: The UCSB Computer Science department has eliminated the GRE requirement for Fall 2022 applicants. More information .

Copyright © UCSB NLP Group 3530 Phelps Hall University of California, Santa Barbara Santa Barbara, CA 93106-5110

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The Natural Language Processing Track

Natural Language Processing

The Natural Language Processing (NLP) track is intended for students who wish to gain expertise in NLP technologies and applications. NLP technologies are of central importance in automating the analysis of text and speech databases and in enabling man-machine interactions through natural language. This track will help you develop leading-edge knowledge of these technologies.

SUMMARY OF REQUIREMENTS

• Complete a total of  30 points  (Courses must be at the 4000 level or above)
• Maintain at least a  2.7  overall GPA. (No more than 1 D is permitted).
• Complete the  Columbia Engineering Professional Development & Leadership (PDL)  requirement
• Satisfy  breadth requirements
• Take at least  6 points  of technical courses at the 6000 level
• At most, up to 3 points  of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Submit the  Non-CS/NonTrack form  and the course syllabus to your CS Faculty Advisor for review

1. Breadth Courses

Visit the breadth requirement page for more information.

2. Required Track Courses

Students are required to complete the following 3 courses. Students who have taken equivalent courses in the past and received grades of at least a B may apply for waivers and take other CS courses instead.

3. Elective Track Courses

Students are required to complete 2 courses out of the following list; at least 1 course must be a 6000-level CS course. Since other departments vary their offerings considerably from year to year, it is possible to count such courses toward the MS degree; please propose courses you think might be suitable to your track advisor.

* Due to significant overlap, students can receive credits for only one of these courses (either COMS W4771 Machine Learning or COMS W4721 Machine Learning for Data Science). ** STAT 4001 taken Spring 2018 or prior may count as a substitute for Probability and Statistics/Advanced Data Analysis

4. General Electives

Students must complete the remaining credits with General Elective Courses at the 4000 level or above. At least three of these points must be chosen from either the Track Electives listed above or from the CS department at the 4000 level or higher. Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.

• Non-CS/Non-Track: CS MS students may request up to 3 points of Non-CS/Non-Track points to count toward their 30-point MS program. CS Track advisor may review and approve if the course is determined to be relevant to the CS MS track and sufficiently technical in nature. Students should send the Non-CS/Non-Track Form and the course syllabus to your Track Advisor for review.

*Due to a significant overlap in course material, MS students not in the Machine Learning track can only take 1 of the following courses – COMS 4771, COMS 4721, ELEN 4903, IEOR 4525, STAT 4240, STAT 4400/4241/5241 – as part of their degree requirements.

**Known non-track CS course**

CSOR E4995 Topics in Computer Science and IEOR – Financial Software Systems

Please note:

Students who waive track requirements by using previous courses must still complete 30 graduate credits. this can be done by expanding their elective selection to include courses listed as required track courses and elective track courses; or by taking other graduate courses, the  degree progress checklist  should be used to keep track of your requirements. if you have questions for your track advisor or cs advising, you should have an updated checklist prepared, track planning.

Please visit the Directory of Classes to get the updated course listings. Please also note that not all courses are offered every semester or even every year. A few courses are offered only once every two or three years or even less frequently.

Updated 10/17/2023

Find open faculty positions here .

Computer Science at Columbia University

Upcoming events, in the news, press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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My experience with Simplilearn was great. The curriculum is excellent, and the video lessons are incredible. The curriculum and course content is very relevant to the industry currently. The instructors were also very knowledgeable and helpful.

Diego Sabajo

My learning experience with Simplilearn was outstanding. The course material is very thoughtfully designed. The way of explaining was simple and easy to understand. The instructors were good; the support was really helpful. I recommend this course to others who want to start something in the AI domain.

Shekhar Lele

The certification helped me with my career. After finishing the course, I was assigned new projects within my organization. My long-term career goal is to become a leader in the new age of tech for the upliftment of the needy. The course will really help me do it.

Radhakrishna Mothukuri

The course structure with Simplilearn is a well-stitched program. The content is truly something to appreciate. Industry experts conducted the classes. These experts as instructors made all the difference with their live examples, success stories, etc.

Aswin Thotapalli

My course ended in July this year, and I have already started applying the skills I learned from the course to my business. While I was taking the course, I was able to solve a significant problem for a client. The instructors are industry experts and not just teachers.

Kanchan Khatri

Simplilearn’s course was a path breaker for me. It gave me a lot of guidance and vision to build my career in this niche. I could grab a job as Blockchain Developer at Idea Usher. After completing this course, I became a blockchain enthusiast and started my own Instagram page as a web3 influencer.

After the course completion, I switched my job to TCS as a Java developer, and now I am working as a Systems Engineer. I got a 20% salary hike as well.

Logesh Muruganandham

This certification in blockchain helped me to get updated with the technology trends. The course has been beneficial in improving my portfolio. The instructors are knowledgeable and quickly gel up with the participants.

Kunal Khatri

The live classes were at par with the self-learning videos, and we learned how to code smart contracts and many other things. I liked the live classes' one-to-one interaction with the teacher and doubt-solving sessions.

This team is the best. I love the format and logistics so much that I would love to enroll in more courses. Awesome team!

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This course gave me the basic knowledge required to start building my own models, from organizing and selecting the data to run and testing the models. Also, the trainers were very clear when explaining and gave us lots of tips.

The sessions were informative, concise and clear. Complex concepts like Blockchain were made simple to understand.

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My learning experience with Simplilearn was great. After the course completion, I created an immutable supply chain for my clients, which helped streamline my business operations. I could get more clients, and my overall earnings increased by 80%.

FAQs On NLP Certification Courses

What is natural language processing in 2023.

NLP is a subfield of AI and machine learning for reading, deciphering, and comprehending human speech. It is a technology allowing devices to imitate dialogue and understand messages in content and voice form. It operates computer software that transforms messages from one language to another, encapsulates them, and reacts to verbal orders in real time.

If anyone has used a smartphone, they must have used NLP as a voice-operated GPS, virtual attendant, voice-to-text transcription tool, and chatbot. However, it has a significant part in company solutions by integrating processes, enhancing staff capacity, and streamlining strategic procedures.

The most important NLP programs consist of the following:

Speed Perception

Part Of Language Tagging

Word Meaning Disambiguation

Named System Understanding

Sentiment Computation

Natural Speech Generation

Co-Reference Solution

Which NLP Course is Highly Prestigious?

Also known as Neuro-Linguistic Programming, NLP is a mixture of instruments and ideas employed for teaching Business Analytics . The system was founded on the belief that a person can alter their opinions and actions to attain particular outcomes.

The method is ideal for personal advancement, occupation advice, and relation teaching. Because of these reasons, it is hard for an individual to pick the most suitable NLP credential program. To help students decide easily, we have prepared a checklist of the leading six NLP lessons and diploma programs to study this year.

Simplilearn Nlp Courses

Microsoft: Study Natural Speech Process

Microsoft Nlp Certification : Azure Ai Fundamentals

Improved Credential Programme In Machine Learning And Nlp

Natural Language Processing With Deep Understanding

What are the top NLP courses to pursue?

The top NLP courses that learners can pursue are:

Natural Language Processing Course

Postgraduate Program In AI And Machine Learning

Artificial Intelligence Engineer Course

Deep Learning Course

Is NLP Course in High Demand?

Natural Language Processing (NLP) is a component of architecture, speedily becoming a highly in-demand job in the technological field in recent times. With the advancing automation, the requirement for NLP architects is also rising, and their paychecks are. Whether healthcare, finance, or e-commerce, all industries require NLP creators to determine, resolve, and identify problems accurately.

The rising requirement for NLP courses is mainly attributed to the increasing usage of speech-activated special attendants, chatbots, and other interfaces. Since businesses find it convenient to resolve consumer inquiries by incorporating such interfaces on the webpage, it has become an in-demand job option, which is anticipated to expand by $26.4 billion the following year.

Is there a Future in NLP in 2023?

Although NLP is in high demand today, its future is unpredictable. But it is tough to deny that it will remain a constant element of our life since it allows machines and computers to comprehend our speech. Whether Alexa, Google, or Siri, people will require a permanent personal assistant to do small tasks.

This technology already powers many of the everyday things we do. So, for those wondering whether to step into this field, it is the perfect time. The future of taking the best NLP courses is favorable, as people will need machines that can interpret their dialect and make interaction easier.

Although its development has been relatively slow, it has climbed to success with massive strides in recent times.

Is NLP Developer a Well-paid Job?

Those who are wondering how much they can make yearly by obtaining Natural Language Processing Certification , there is wonderful news waiting for them. The annual earnings of an NLP creator change depending on the firm's reputation, knowledge, position, mastery, and job location. Since they have several job options, go for the one that fits them perfectly.

However, their average salary ranges between $1,16,708 to $7,36,609 . If they work for a well-paying industry and have experience, their earning potential will be higher than the rest.

What are the Two Main Areas of NLP?

The two main areas of the NLP course are Syntax and Semantic analysis. Here is a small table to help understand their differences in a better way:

Is NLP the Fate of Artificial Intelligence?

NLP is a subfield of Artificial Intelligence that facilitates systems to discern, interpret, and understand human speech. Because of the progress in AI chips, firms can develop more complicated NLP prototypes. NLP certification will favorably influence the contributions and acceptance pace of technology.

Soon, informal AI devices might be brighter and easily acknowledge the implication of our languages, all because of the improvements in NLP models. It will let firms automate responding to consumer requests. Learning Business Strategies will help produce authentic content on any subject with the help of Artificial Intelligence.

What are the Leading Language Structures for NLP in 2023?

Language structures help create NLP software. They use transmission learning approaches to carry out\ different NLP procedures on varied datasets. Since this technology is utilized across various industries, here are the leading language structures for NLP programs 2023:

Bert (Bidirectional Encoder Representations From Transformers)

Gpt-2 By Open Ai Training Course

Lamda (Language Models For Dialogue Applications) By Google

Palm (Pathways Language Model) By Google

Roberta (Robustly Optimized Bert)

T5 (Text-To-Text Transfer Transformer)

Which Language is Highly Suitable for NLP?

Out of all the speech structures, Java and R are the most frequently used ones for NLP. However, the highly suitable language for NLP is Python, due to its vast data collection, straightforward syntax, translucent semantics, and capacity to accommodate other programming styles. For those planning to take NLP training , try Python, as it will also diminish their understanding curve.

Python offers developers a broad collection of tools and data centers to manage NLP-related jobs, such as data distribution, subject modeling, part-of-speech (POS) identifying, word vectors, and sentiment study.

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