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Ling 581: Computational Linguistics (Spring 2008)
This course will serve as an introduction to the field of computational linguistics. The course begins with an introduction to finite-state automata and some basic natural language applications; this is extended to finite-state transducers with applications in phonology and morphology. Other topics covered: basic concepts of speech processing, the Viterbi algorithm, ngram language models, part of speech tagging, context-free grammars and context-free parsing, and information retrieval.
| Semester | Spring 2008 |
|---|---|
| Course type | Lecture / Lab |
| Instructor | Rob Malouf |
| Time | MWF 11:00–11:50 |
| Location | BA 251 |
Requirements
The final grade will be based on homework assignments (30%), a take-home midterm exam (30%), and a take-home final exam (40%). Through the term, there will be occasional homework assignments to practice the techniques learned in class. Working in groups is encouraged, but please include the names of all coworkers on the assignment. The mid-term and final, though, should be done individually.Sample programs discussed in class will be in Python. Students may use any language for programming exercises.
Readings
The required textbooks for this course are:Dan Jurafsky and James Martin. 2002. Speech and Natural Language Procrocessing. Blackwell. http://www.cs.colorado.edu/~martin/slp.htmland
Kenneth R. Beesley and Lauri Karttunen. 2003. Finite State Morphology. CSLI Publications. http://www.fsmbook.comBoth books are available in the campus bookstore and at Amazon, etc.
Additional readings will be made available in class or via the ``Resources'' section of the course web page.
Proposed schedule
| Week 1 | Introduction, basic concepts |
| Week 2–3 | Regular expressions and finite state machines |
| Week 4–6 | Computational morphology |
| Week 7 | Probability and information theory |
| Week 8 | n-gram models |
| Week 9–10 | Hidden Markov Models and Viterbi algorithm |
| Week 11–12 | Part of speech tagging |
| Week 13–14 | Information retrieval and information extraction |
| Week 15 | Wrap-up and review |