Master of Science in Computational Linguistics
The Computational Linguistics MS program (MSCL) 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.
Graduates from the Computational Linguistics MS program will be prepared for both further training at the PhD level in Computer Science and Linguistics and Computational Linguistics positions in industry. A growing number companies such as Google, Amazon.com, Nuance, LexisNexis, Oracle and many others are searching for employees with advanced degrees in Computational Linguistics for positions ranging from speech recognition technology to improving translation systems to developing better models of language understanding.
The curriculum consists of courses in linguistics and computer science, in roughly a 50/50 mix, for a total of 32 credit hours. Four courses (16 credits) are required in linguistics and four courses (16 credits) in computer science. The degree further requires a culminating special written project on a topic relevant to the student's interest and in consultation with individual advisors.
The degree can typically be completed in three full-time semesters.
Within linguistics, students will work with an adviser to create a “track” for their coursework in one of three areas:
- Sound structure (LIN 410, 427, 510)
- Grammatical structure (LIN 420, 460, 461, 462, 520)
- Meaning (425, 465, 466, 468, 525, 535)
Students will be encouraged to take LIN 450 and LIN 501 as it suits their programs.
Students with no linguistics background are required to take the following “bridging” course, which does not count towards the degree. Individual courses may have their own additional prerequisites.
- LIN 110: Introduction to Linguistic Analysis
At least one of the following:
- LIN 410: Introduction to Language Sound Systems
- LIN 420: Introduction to Grammatical Systems
- LIN 425: Introduction to Semantic Analysis
Plus at least two from the following:
- LIN 427: Topics in Phonetics and Phonology
- LIN 450: Data Science for Linguistics
- LIN 460: Syntactic Theory
- LIN 461: Phrase Structure Grammar
- LIN 462: Topics in Experimental Syntax
- LIN 465: Formal Semantics
- LIN 466: Pragmatics
- LIN 468: Computational Semantics
- LIN 501: Methods in Linguistic Research
- LIN 520: Syntax
- LIN 525: Graduate Semantics
- LIN 527: Topics in Phonetics and Phonology
- LIN 535: Formal Pragmatics
Students may be required to take the following “bridging” courses that will not count towards their degree. Individual courses may have their own additional prerequisites.
- CS 172: The Science of Data Structures
- MTH 150: Discrete Math
- CS 173: Computation and Formal Systems
- CS 242: Artificial Intelligence
- CS 447: Natural Language Processing
- CS 448: Statistical Speech and Language Processing
Plus at least two of the following:
- CS 444: Logical Foundations of Artificial Intelligence
- CS 446: Machine Learning
- CS 549: Topics in Artificial Intelligence