Dalhousie University    [  http://web.cs.dal.ca/~vlado/csci6509  ]
Winter 2013 (Jan7-Apr8)
Faculty of Computer Science
Dalhousie University

CSCI 4152/6509 — Natural Language Processing

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Time: Mondays, Wednesdays, and Fridays, 12:35-13:25; labs on Wednesdays 13:35-14:25 as needed
Location: McCain Arts & SS 2017; Labs in Teaching Lab 2, Goldberg CS building
Instructor: Vlado Keselj, office: CS bldg 326, phone: 494-2893, e-mail: vlado@cs.dal.ca
Office hours: "Open-door" policy, unless in a meeting or on a phone call. To be sure that I am available, please make an appointment by e-mail.
TA: Jacek Wolkowicz, email: jacek@cs.dal.ca
E-mail list: nlp-course@lists.dnlp.ca

Course Description

Natural Language Processing (NLP) is an area of Computer Science, and sub-area of Artificial Intelligence, concerned with the problem of automatically analyzing and generating a natural language, such as English, French, or other, in written or spoken form. This course introduces fundamental concepts and principles used in NLP with emphasis on two approaches to NLP: statistical and unification-based. Some applications are discussed, such as the problems of question answering, machine translation, text classification, information extraction, grammar induction, and dictionary generation.

Links to calendar descriptions: CSCI 4152, and CSCI 6509.

Evaluation Scheme (CSCI 4152)

40% Assignments (programming and some theory)
30% Final exam (or Test) on core material
10% Class Presentation and Participation
20% Project Report (possibly implementation oriented)

Evaluation Scheme (CSCI 6509)

30% Assignments (programming and some theory)
30% Final exam (or Test) on core material
10% Class Presentation and Participation
30% Project Report (research oriented)
Academic Integrity Policy

Course Calendar
NLP Research Links
Instructions for submitting programming parts of assignments on the host bluenose.

References

Required Textbook:
  1. Speech and Language Processing by Daniel Jurafsky and James H. Martin, Prentice-Hall, Inc., 2008, ISBN 978-0-13-187321-6. http://www.cs.colorado.edu/~martin/slp.html.
Recommended Reading (in tentative decreasing relevance order):
  1. Learning Perl, 5th Edition by Randal L. Schwartz, Tom Phoenix, Brian D. Foy, O'Reilly, 2008, ISBN 978-0-596-52010-6. Available on-line from Dalhousie:  http://proquest.safaribooksonline.com/9780596520106.
  2. Natural Language Processing with Python by Steven Bird, Ewan Klein, Edward Loper, O'Reilly, 2009, ISBN 978-0-596-51649-9. http://oreilly.com/catalog/9780596516499/.
  3. Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schuetze, The MIT Press, 1999, ISBN 0-262-13360-1. http://www-nlp.stanford.edu/fsnlp/.
  4. Syntactic Theory: A Formal Introduction by Ivan A. Sag and Thomas Wasow, CSLI Publications, Stanford, 1999, ISBN 0-521-58388-8.
  5. Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Addison Wesley, 1999, ISBN 020139829X.
  6. Pattern Recognition and Machine Learning by Chrisopher M. Bishop, Springer, 2006, ISBN 0-38-731073-8.
  7. Statistical Language Learning by Eugene Charniak, The MIT Press, 1993.
  8. Statistical Methods for Speech Recognition by Frederick Jelinek, The MIT Press, 1997.
  9. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, 2nd edition, Prentice Hall, 2003, ISBN 0-13-790395-2. http://aima.cs.berkeley.edu/.
© 2002-2013 Vlado Keselj, last update: 15-May-2013