This is an introductory course to computational linguistics, centered on the fundamental
questions of how a machine can learn to analyze, understand and produce language. The
topics covered include speech synthesis and recognition, syntactic parsing, semantic
interpretation, discourse and pragmatic inference, and sentiment analysis. Students will
get familiar with standard practical tools and resources for automatic linguistic analysis.
Prerequisites:
Undergraduate students should have completed CIS 121 before enrolling.
Textbooks:
[REQUIRED] Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit, O'Reilly Media, 2009. (Free Online)
[OPTIONAL] Daniel Jurafsky and James H. Martin Speech and
language processing: An Introduction to Natural Language Processing,
Computational Linguistics, and Speech Recognition, 2nd edition, Pearson Prentice Hall, 2008. (Available on Amazon)
[OPTIONAL] Chris Manning and Hinrich Shutze, Foundations
of Statistical Natural Language Processing, MIT Press, 1999. (Free Online)
Download slides here
Sep 7, Lecture 1: Welcome to CIS 430/530 (Slides: [PPTX][PDF])
Sep 12, Lecture 2: From Frequency to Meaning: Vector Space Models of Semantics (Slides: [PPTX][PDF]) [Related Reading]
Sep 14, Lecture 3: Introduction to Language Models (Slides: [PPT][PDF])
Sep 19, Python/NLTK/matplotlib Tutorial, Part 1 (Slides: [PPT][PDF])
Sep 21, Python/NLTK/matplotlib Tutorial, Part 2
Sep 26, Lecture 4: Language Models II (Slides: [PPT][PDF])