Top Ranked Graduate Programs in Computational Linguistics
Ranked as: #34 in Best National University
The Computational Linguistics MS program at Rochester trains students to be conversant both in the analysis of language and in computational techniques applied to natural language. The curriculum consists of courses in Linguistics and Computer Science for a total of 32 credit hours. The degree can typically be completed in three full-time semesters.
Ranked as: #35 in Best National University
The master degree program (MS) in computational linguistics is an intensive two-year program that prepares you for mastery in the field, whether you want to work in industry or pursue your PhD. The computational linguistics training at Brandeis enables both initial success and satisfying long term careers in this burgeoning field. The first year curriculum is tailored to the level of the students entering the program.
The Master of Science Program in Computational Linguistics is a specialized degree offered by the Department of Computer Science. We offer an accessible, intensive two-year curriculum for students who have a linguistics, language, computer science, mathematics, or science background, as well as students without prior study of computer science or linguistics. Along with the two-year master's program for students entering with a bachelor's degree, we also offer a five-year combined bachelor's/master's program for Brandeis undergraduates studying computer science or linguistics.
Computational linguistics is a burgeoning field, and skills are in high-demand in many areas, including speech recognition, artificial intelligence, machine translation, big data, automated text analysis and web search. Brandeis offers three graduate degree programs for students interested in this field. The two-year Master of Science in Computational Linguistics program is an accessible, intensive two-year curriculum for students who have a linguistics, language, computer science, mathematics, or science background, as well as students without prior study of computer science or linguistics.
The Brandeis undergraduate linguistics program focuses on theoretical generative linguistics. It offers both a major and minor, with core courses on phonological theory, syntactic theory, formal semantics, and formal pragmatics, as well as a range of electives. The two-year master's degree program in computational linguistics is designed for outstanding students, preferably with an undergraduate degree in linguistics, computer science or the study of language.
The two-year Master of Science program in Computational Linguistics is designed for outstanding students with an undergraduate degree in linguistics, computer science, the study of language, or a related field. This interdisciplinary MS provides a solid foundation for professional work in the field or pursuit of a PhD in computational or theoretical linguistics.
Ranked as: #53 in Best National University
The Computational Linguistics concentration area at the UT Linguistics department is structured as follows. In their first year, graduate students interested in computational linguistics usually take the required courses related to syntax and semantics (some students opt to take Semantics I in their second year). Beginning in their second year, students interested in continuing in computational linguistics choose advanced courses and seminars in computational linguistics, as well as courses from other departments.
The Computational Linguistics concentration area educates the student in the theory, technologies and applications of Computational Linguistics and Natural Language Processing (NLP). Computational Linguistics is an interdisciplinary field addressing human languages by ing methods of both Linguistics and Computer Science. Research in Computational Linguistics addresses the computational properties of linguistic models of natural language and develops algorithms and computational implementations of such linguistic models 2. Research in NLP emphasizes the goal of developing systems that can deal effectively with natural language data in an application context.
Jessy Li: Computational Linguistics, e.g., discourse processing, information organization, and text intelligibility. Katrin Erk: Computational semantics, in particular in-depth sentence semantics, and computational lexical semantics.
Ranked as: #60 in Best National University
Computational Linguistics (also called Natural Language Processing, abbreviated as NLP) is a field of vital importance in the information age. With growing amounts of speech and text data, the demand keeps increasing for automated tools to understand human language and NLP specialists to develop and operate these tools. In industry, Computational Linguistics techniques are being widely used in search engines, digital libraries, speech recognition systems, and data mining toolkits.
Engage in recent data-driven scholarship in computational social sciences and digital humanities. Computational Linguistics (also called Natural Language Processing, abbreviated as NLP) is a field of vital importance in the information age. Nine courses (five 3-credit LIN courses in linguistics, two 3 credit CPS courses in computational science, and two 3 credit IST courses in information studies) plus a 3 or 6 credit IST internship, all offered on a yearly basis, will be required of all those interested in receiving the degree.
Ranked as: #92 in Best National University
The Departments of Linguistics and Computer Science have teamed up to jointly offer an interdisciplinary degree, the Computational Linguistics, Analytics, Search and Informatics Professional Master’s Degree (CLASIC), approved by the University of Colorado Board of Regents in April 2016. CLASIC, a stand-alone Professional Master of Science degree, provides students with a solid foundation in both linguistics and computer science graduate course work as well as several courses focused on date-driven linguistics, computational linguistics, and information processing. CLASIC students complete a two-year degree including a 2-hour capstone project that runs in conjunction with an internship or CU based research project.