Main Contributing Faculty:

Eugene Charniak Professor of Computer Science and Cognitive and Linguistic Sciences.
Mark Johnson Professor of Cognitive and Linguistic Sciences and Computer Science.

In addition, there are several other faculty whose research is closely related to computational linguistics. Undergraduate and graduate students majoring in computational linguistics would be likely to interact closely with these faculty also.

Stuart Geman Professor of Applied Mathematics
Thomas Hofmann Assistant Professor of Computer Science

What is computational linguistics?

The scientific goal of computational linguistics is to understand the acquisition, comprehension and production of human languages in information processing terms. Because language is used to convey information we assume that these processes fundamentally involve the processing of information, i.e., that they are fundamentally computational in nature. Computational linguistics also has a more applied, technological side: if we understand the information processing involved in human language, we can also implement it on computers. Applications of computational linguistics include:
  • Machine translation (i.e., translating documents from one language to another by computer)
  • Speech recognition (e.g., transcribing speech)
  • Information extraction (e.g., automatically identifying the topic of a document, the things that it talks about, and the important relationships between those things)
Even after the bubble, there is a steadily increasing demand for people with training in computational linguistics in the software industry.

Specializing in computational linguistics at Brown

There is no separate concentration in Computational Linguistics; students who wish to specialize in Computational linguistics typically concentrate in Cognitive Science, Computer Science or Linguistics (double majoring in Computer Science and Linguistics is very common).

There are two courses that every student intending to specialize in Computational Linguistics should take (they do not have to be taken in order).

Students specializing in computational linguistics should have a reasonable background in linguistic theory, especially natural language syntax and semantics.
  • CG131 Introduction to Syntax
  • CG113 Introduction to Formal Semantics
Computational skills are of course very useful for computational linguistics. In addition to programming courses, relevant courses include:
  • CS22 Introduction to Discrete Mathematics
  • CS51 Models of Computation
  • CS141 Introduction to Artificial Intelligence
  • CS181 Computational Molecular Biology (it turns out that computational linguistics techniques also get applied here)
  • CS295:3 Machine Learning and Pattern Recognition
Finally, statistical methods are now absolutely essential for modern computational linguistics. We are lucky to have several statisticans here at Brown who specialize in stochastic grammars and other kinds of models of sequences. The following courses are highly recommended for anyone specializing in computational linguistics.
  • AM165/166 Statistical Inference (the basic course)
  • AM169 Computational Probability and Statistics
  • AM171 Information theory


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