M.S. in Computational Linguistics
The M.S. in Computational Linguistics brings together the study of linguistics, linguistics theory, and computer analysis. Students deepen their knowledge of how language works while developing technical skills to build and apply automated tools that understand human language, aggregate and find meaning in massive amounts of text and voice data, and learn from past interactions with humans.
In order to receive the Masters of Science in Computational Linguistics, students must complete at least 36-credit hours of coursework, which may include up to 6 credits earned through an internship, and earn a cumulative grade point average of at least 3.0.
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. The first of these courses, Introductory Linguistic Analysis, will provide essential grounding in the mechanics of language, e.g. the sound system, word structure, sentence structure, and meaning. Through the use of examples from a range of languages, students will learn about similarities and differences across languages, which will allow them to understand the various possible manifestations of natural language. Syntactic Analysis, Morphological Analysis, Semantics of Human Languages, build on the principles learned in Introductory Linguistic Analysis to provide students with a deeper understanding of the three areas of linguistics that are most important to the field of computational linguistics. Advanced Syntax, builds upon the principles of syntactic analysis which are introduced in Syntactic Analysis.
Three required courses in information studies, including two foundational courses Information Retrieval and Natural Language Processing and one course Internship in Information Studies. The internship in Information Studies can be taken for three or six credits. If taken for three credits, an elective from the courses below for three credits needs to be added. Basic of Information Retrieval, will provide fundamental knowledge in information representation, information seeking behavior, query and document matching, relevance measure, search interface design, and information retrieval system evaluation. Natural Language Processing, introduces concepts and methods in processing text at syntactic, semantic, and pragmatic levels. It covers techniques of tokenizing, sentence splitting, part-of-speech tagging, and parsing.
Two required courses in computational science Beginning Explorations in Computing and Programming and Intermediate Programming and Computing Fundamentals. Students who demonstrate sufficient knowledge in these areas may test out of the courses and replace them with elective courses from the list below.