CSCE 5290/493 Natural Language Processing, Fall 2013

NEW: Final Exam Demos: Thu. Dec 12, 11:00am-1:00pm - see UNT schedule

Instructor:

Paul Tarau, Professor - see my home page for contact info and office hours. Teaching assistant: Iris Gomez-Lopez, see her NLP page for submitting assignments and exams.

Objectives

This course will cover traditional material, as well as recent advances in the theory and practice of natural language processing (NLP) - the creation of computer programs that can understand, generate, and learn natural language. The course will introduce both knowledge-based and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems.

Syllabus

  1. Course overview
  2. Short Perl tutorial
  3. Linguistics Essentials
  4. Working with the Stanford NLP tool set
  5. Language Models I Chap.4 [Jurafsky & Martin]
  6. Language Models II Chap.4 [Jurafsky & Martin]
  7. Language Models III Chap.4 [Jurafsky & Martin]
  8. Collocations Chap.5 [Manning & Schutze]
  9. Working with Wordnet and Wikipedia-based resources
  10. Morphological Processing Chap.3 [Jurafsky & Martin]
  11. Word classes and part of speech tagging I Chap.5 [Jurafsky & Martin]
  12. Word classes and part of speech tagging II Chap.5 [Jurafsky & Martin]
  13. HMM Tagging. Viterbi Algorithm. Chap.6 [Jurafsky & Martin]
  14. Context Free Grammars Chap.12-13 [Jurafsky & Martin]
  15. Working with Definite Clause Grammars
  16. Parsing with Context Free Grammars Chap.12-13 [Jurafsky & Martin]
  17. Logic form transformation
  18. Probabilistic Parsing Chap.14 [Jurafsky & Martin]
  19. Montague Grammars and Semantics
  20. Graph-based Natural Language Processing
  21. Word Sense Disambiguation I Chap. 19, 20 [Jurafsky & Martin]
  22. Word Sense Disambiguation II Chap.19, 20 [Jurafsky & Martin]
  23. Word Sense Disambiguation III Chap.19, 20 [Jurafsky & Martin]
  24. Text semantic similarity
  25. Sentiment analyisis

Directory for slides, assignments and other resources.

Textbook

Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (second edition)by D. Jurafsky and J. Martin

Recommended reading:

Foundations of Statistical Natural Language Processing by C. Manning and H. Schutze

Evaluation: