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Introduction to Computational Linguistics

Courses

Introduction to Computational Linguistics

This course provides an overview of the main methods and algorithms used in computational linguistics, motivated by some examples of questions they can be used to investigate.  We will cover the basics of: information theory (entropy and mutual information), n-gram models (for computing the probabilities of phone or word sequences), finite-state automata and hidden Markov models, parsing algorithms, and distributional semantic models.  In addition to lectures, we will include some hands-on labs in Python to help students gain practical experience with some of these concepts.

Required textbook:
Speech and language processing, 2nd edition, by D. Jurafsky and J.H. Martin (2014).

Course Status: Closed

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Course Number:

102

Course Session:

Four-week Session

Times:

Monday: 8:30 am-10:20 am
Thursday: 8:30 am-10:20 am

Instructor(s):

Prerequisites:

Students should have some previous programming experience in Python and be familiar with the basics of probability theory (e.g., joint and conditional probability, Bayes' Rule).