Computational Linguistics PPT PDF SLIDES
Computational Linguistics PPT PDF SLIDES
Course description:
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])
- Sep 28, Lecture 5: Morphology (Slides: [PPT] [PDF])
- Oct 3, Lecture 6: Part of Speech Tagging (Slides: [PPT] [PDF])
- Oct 5, Lecture 7: Hidden Markov Models (Slides: [PPT] [PDF])
- Oct 12, Lecture 8: Dynamic programming algorithms (Slides: [PPT] [PDF]
- Oct 17, Lecture 9: Formal Grammars (Slides: [PPT] [PDF])
- Oct 19, Lecture 10: Parsing (Slides: [PPT] [PDF])
- Oct 24, Lecture 11: Statistical parsing (Slides: [PPT] [PDF])
- Oct 26, Lecture 12: Automatic summarization (Slides: [PPTX] [PDF])
- Oct 31, Lecture 13: Content selection (Slides: [PPTX] [PDF])
- Nov 2, Lecture 14: Evaluation (Slides: [PPTX] [PDF])
- Nov 7, Lecture 15: Word sense disambiguation (Slides: [PPT] [PDF])
- Nov 9, Lecture 16: Word similarity (Slides: [PPT] [PDF])
- Nov 14, Lecture 17: Semantic Roles (Slides: [PPT] [PDF])
- Nov 16, Lecture 18: Coreference resolution (Slides: [PPT] [PDF])
- Nov 20, Lecture 19: Discourse coherence (Slides: [PPT] [PDF])
- Nov 30, Lecture 20: Discourse coherence and readability (Slides: [PPT] [PDF])
- Dec 5, Lecture 21, Guest lecture by Annie Louis: Twitter NLP (Slides: [PDF])
