Introduction to Computational Linguistics

LING 569 — LSA Linguistic Institute — Summer 2013
Prof. Jason Eisner (Johns Hopkins University)

parse trees



Vital Statistics [1]

This class presents fundamental methods of computational linguistics. We will develop probabilistic models to describe what trees and sequences 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.

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.

When:TuTh 1:30-3:20 (also Fr 6/28 1:00-5:00)
Where:Mason Hall 1401
Prof:Jason Eisner
TA:Darcey Riley
Office hrs:TBA, in Mason Hall 2475 or 2455. May include a weekly dinner with interested students.
Discussion site:
Web page:
Textbook: Jurafsky & Martin, 2nd ed. (useful but not required)
Policies and grading:TBA


Note: Lecture schedule below is tentative. Assignments and readings will be added.

Note: Additional slides plus more computationally intensive assignments are available from my NLP course at Johns Hopkins, which is more than twice as long and assumes more computational background (but no prior linguistic background). Here's an old list of other NLP courses.

Tue 6/25: Introduction to Probability

Thu 6/27: Modeling and Smoothing

Fri 6/28 (Special Lab 1-5pm): Grammar Writing

Tue 7/2: Bayes' Theorem; Beyond Simple Context-Free Grammars

Fri 7/5: Parsing

Tue 7/9: Hidden Markov Models

Thu 7/11: It's All About High-Probability Paths in Graphs

Tue 7/16: Sequence Modeling with Finite-State Machines

Thu 7/18: NLP Tasks and Evaluation