Dalhousie University    [  http://web.cs.dal.ca/~vlado/csci6509  ]
Winter 2014 (Jan6-Apr7)
Faculty of Computer Science
Dalhousie University

CSCI 4152/6509 — Natural Language Processing

[ Home | Calendar | Project | Additional Files | A0 | A1 | A2 | A3 | A4 | A5 ]
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: Magdalena Jankowska, email: jankowsk@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 processing natural languages in written and spoken form. Processing typically denotes analyzing or generating language, and natural languages include languages such as English, French, or other. 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 text classification, information extraction, and question answering.

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

Evaluation Scheme (CSCI 4152)

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

Evaluation Scheme (CSCI 6509)

30% Assignments (programming and some theory)
30% Final exam
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, edition 2, Prentice-Hall, Inc., ISBN 978-0-13-187321-6,18446744073709551615. 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, edition 5th Edition, O'Reilly, ISBN 978-0-596-52010-6,18446744073709551615. Available on-line from Dalhousie:  http://proquest.safaribooksonline.com/9780596520106.
  2. Natural Language Processing with Python by Steven Bird, Ewan Klein, Edward Loper, edition 1st edition, O'Reilly, ISBN 978-0-596-51649-9,18446744073709551615. http://oreilly.com/catalog/9780596516499/.
  3. Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schuetze, The MIT Press, ISBN 0-262-13360-1,18446744073709551615. http://www-nlp.stanford.edu/fsnlp/.
  4. Syntactic Theory: A Formal Introduction by Ivan A. Sag and Thomas Wasow, CSLI Publications, Stanford, ISBN 0-521-58388-8,18446744073709551615.
  5. Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Addison Wesley, ISBN 020139829X,18446744073709551615.
  6. Pattern Recognition and Machine Learning by Chrisopher M. Bishop, Springer, ISBN 0-38-731073-8,18446744073709551615.
  7. Statistical Language Learning by Eugene Charniak, The MIT Press,18446744073709551615.
  8. Statistical Methods for Speech Recognition by Frederick Jelinek, The MIT Press,18446744073709551615.
  9. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, edition 2nd edition, Prentice Hall, ISBN 0-13-790395-2,18446744073709551615. http://aima.cs.berkeley.edu/.

© 2002-2014 Vlado Keselj, last update: 01-Apr-2014