Machine Learning for Natural Language Processing

This course offers a survey of basic statistical and machine learning methods for computational linguistics. Topic to be covered include:

• generative and discriminative probabilistic models
• naive Bayes classifiers
• maximum entropy models
• decision trees
• memory based learning
• committee machines
• support vector machines

Prerequisite: Basic knowledge of probability and statistical NLP

Suggested Readings

• Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall.

• Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press.

• Ian H. Witten, Eibe Frank, and Mark A. Hall. 2011. Data Mining: Practical Machine Learning Tools and Techniques. 3rd edition. Morgan Kaufmann.

• Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing in Python. O'Reilly Media.

• James Pustejovsky and Amber Stubbs. 2012. Natural Language Annotation for Machine Learning. O'Reilly Media.

Lectures

1. Introduction (slides)
2. Bayesian methods (slides)
3. Non-parametric techniques and ensemble algorithms (slides)
4. Support vector machines (slides)
5. Python examples (notebook)