The course will cover fundamental concepts and algorithms in computational linguistics and natural language processing. We will explore computational analysis from morphological level to word level all the way to the cross-linguistic level. We will cover finite state machine, n-gram language model, part-of-speech tagging, probabilistic statistical parsing, computational distributional lexical semantics, and machine translation. The lab component of the course will introduce necessary mathematical concepts such as conditional probability, Bayes’ Rule, Maximum Likelihood Estimation, Bayesian estimation, vector space, matrix operations, and inferential statistics. The assignments will be done in Python. If you do not know how to program in Python before, you will have to learn quickly during the first week of the class.
This course is required for Computational Linguistics MS students.
The instructor is Professor James Pustejovsky