Dalhousie University    [  http://web.cs.dal.ca/~vlado/csci6509  ]
Winter 2019 (Jan7-Apr8)
Faculty of Computer Science
Dalhousie University

CSCI 4152/6509 — Natural Language Processing

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Time: Lectures: Tue-Thu-Fri 14:35-15:25; Labs: Wed (4152) B01 13:05-14:25, B02 13:05-14:25, and Fri (6509) B01 10:35-11:55
Location: Lectures: LSC-Psychology P5260; Labs: (4152) B01 CS-143 (TLab 2), B02 CS-133 (TLab 1), and (6509) B01 CS-143 (TLab 2)
Instructor: Vlado Keselj, office: CS bldg 432, phone: 902.494.2893, e-mail: vlado@dnlp.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.
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

32% Assignments
10% Presentation and Class Participation
26% Project Deliverables (P0, P1, Report)
32% Final exam
Note: The difference between 4152 and 6509 evaluation is mainly expressed in the project requirements and evaluation.

Academic Integrity Policy

Course Calendar
NLP Research Links


Required Textbook:
  1. Speech and Language Processing by Daniel Jurafsky and James H. Martin, edition 2, Prentice-Hall, Inc., 2013, ISBN 978-0-13-187321-6.. Draft of Edition 3 available at: https://web.stanford.edu/~jurafsky/slp3/  http://www.cs.colorado.edu/~martin/slp.html
Recommended Reading:
  1. Learning Perl, 6th Edition by Randal L. Schwartz, brian d foy, Tom Phoenix, edition 6th Edition, O'Reilly, 2011.. Available on-line from Dalhousie:  http://proquestcombo.safaribooksonline.com/9781449311063?uicode=dalhousie
  2. Natural Language Processing with Python by Steven Bird, Ewan Klein, Edward Loper, edition 1st edition, 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, edition 2nd edition, Prentice Hall, 2003, ISBN 0-13-790395-2.. http://aima.cs.berkeley.edu/

Maintained by: Vlado Keselj, last update: 29-Mar-2019