Covers several important machine learning algorithms for natural language processing including decision tree, kNN, Naive Bayes, transformation-based learning, support vector machine, maximum entropy and conditional random field. Students implement many of the algorithms and apply these algorithms to some NLP tasks." Prerequisite: LING 570. Offered: W.
In this course, we will study statistical algorithms that produce state-of-the-art results on NLP tasks. We will compare supervised learning algorithms that require a lot of training data with the unsupervised ones. We will also study a few important discriminative models. Students will gain hands-on experience by applying these algorithms to real NLP tasks.
Student learning goals
General method of instruction
Prerequisites: LING 570 and LING 571 Stat 391 (Prob. and Stats for CS) or equivalent Programming: - C/C++ or Java - basic unix/linux commands (e.g., ls, cd, ln, sort, head) - Perl (optional): tutorials on Perl
Class assignments and grading