Tag Archives: Intro
Introduction to Computational Linguistics

Jason Eisner – Johns Hopkins University
Course time: Tuesday/Thursday 1:30-3:20 pm AND Friday, June 28 1:00-5:00 pm
1401 Mason Hall

See Course Description

This class presents fundamental methods of computational linguistics. We will develop probabilistic models to describe what structures are  likely in a language.  After estimating the parameters of such models,  it is possible to recover underlying structure from surface  observations. We will examine algorithms to accomplish these tasks.

Specifically, we will focus on modeling
  • trees (via probabilistic context-free grammars and their relatives)
  • sequences (via n-gram models, hidden Markov models, and other probabilistic finite-state processes)
  • bags of words (via topic models)
  • lexicons (via hierarchical generative models)
We will also survey a range of current tasks in applied natural  language processing.  Many of these tasks can be addressed with  techniques from the class.
Some previous exposure to probability  and programming may be helpful.  However,  probabilistic modeling  techniques will be carefully introduced, and programming expertise will  not be required.  We will use a very high-level language (Dyna) to  describe algorithms and visualize their execution.
Useful related courses include Machine Learning, Python 3 for  Linguists, Corpus-based Linguistic Research, and Computational  Psycholinguistics.

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