**Editor for this issue:** Ann Dizdar <dizdartam2000.tamu.edu>

Earlier this year I posted a query about teaching material for a course on statistical NLP, based on Eugene Charniak's book "Statistical Language Learning". Here is a selective summary of the information I received, together with some personal reflections on the material I ended up using in the course. Hopefully, this can be useful for people who find themselves in the same situation. First of all, I would like to thank the following people who responded to the original query: Francois Aumont Kirk Belnap Lars Martin Fosse Wim de Groote James Hearne Marti Hearst Chris Hogan Reinhard Koehler Becky Passonneau Andrew Salway Christer Samuelsson Arian J.C. Verheij Andy Way As mentioned above, the main text book I used for the course (and the only one currently available) was Charniak, E. (1993) Statistical Language Learning. MIT Press. Several people directed me to David M. Magerman's review of Charniak's book (published in Computational Linguistics 21, 103-111). Although the review is rather negative and I tend to agree with many of the reviewer's points, I think Charniak's book is useful for an introductory course on statistical NLP, provided it is supplemented with background reading in probability theory, statistics and information theory. I got many recommendations for textbooks in probability theory, statistics and information theory. Here is a selection of references that may be useful: Probability and Statistics: DeGroot, M. H. (1986) Probability and Statistics. Second Edition. Addison-Wesley. Lindgren, B. W. (1993) Statistical Theory. Fourth Edition. Chapman and Hall. Ross, S. (1994) A First Course in Probability. Fourth Edition. Macmillan. Information Theory: Ash, R. (1965) Information Theory. New York: John Wiley. Cover, T. M. & Thomas, J. A. (1991) Elements of Information Theory. John Wiley and Sons. One problem that I struggled with initially was what to give to the students as background reading, since each of the books listed above contains much too much material for an introductory course. This problem was solved beautifully by Brigitte Krenn and Christer Samuelsson, who let me use their compendium "The Linguist's Guide to Statistics", written for a course on statistical NLP in Saarbruecken. Besides a short but thorough introduction to probability theory, statistics, information theory and Markov models, the compendium also contains a very useful bibliography of statistical NLP work. I can strongly recommend this text. People who would like to use it may contact Christer Samuelsson (christerCoLi.Uni-SB.DE) or Brigitte Kren (krennCoLi.Uni-SB.DE). For the different applications of statistical NLP (tagging, parsing, disambiguation, etc.), I used Charniak's book together with original articles (most of which can be found in the bibliography of Krenn and Samuelsson). One useful source of articles is the special issue of Computational Linguistics on "computational linguistics using large corpora", originally published in Volume 19 of Computational Linguistics (1-2) and later published as a book: Armstrong, S. (ed) (1994) Using Large Corpora. MIT Press. Somebody actually recommended using this book as the main text instead of Charniak. For a course at a more advanced level, I think this would work fine, but you will still need the background material on probability theory, etc. (if the students do not already have this background, of course). Besides literature, I didn't find very much material. Two exceptions are worth mentioning: Chris Brew (at the University of Edinburgh) has a WWW page with teaching materials for statistical NLP, including some simple programs for calculating bigram frequencies, etc. and notes on some of the chapters of Charniak's book. The address is: http://www.cogsci.ed.ac.uk/~chrisbr/charniak.html Chris Manning (at Carnegie-Mellon) has a WWW page containing an "Annotated list of resources on statistical natural language processing and corpus-based computational linguistics". The address is: http://kinks.phil.cmu.edu/manning/statnlp.html Finally, I include the syllabus (plan of lectures + reading list) for the course I ended up teaching. It was an advanced undergraduate course for students with a solid background in (traditional) computational linguistics but little or no background in probability theory and statistics. Statistical Models and Methods in Computational Linguistics Lecture Plan 1. Introduction 2. Elementary Probability Theory 3. Stochastic Variables 4. Statistical Inference 5. Elementary Information Theory 6. Markov Models 7. Probabilistic Language Models 8. Part-of-speech Tagging 9. Probabilistic Grammars 10. Probabilistic Parsing 11. Syntactic Disambiguation 12. Semantics Disambiguation 13. Machine Translation 14. Conclusion Reading List Brown, P. Cocke, J., Della Pietra, S., Della Pietra, V. J., Jelinek, F.,Lafferty, J. D., Mercer, R. L. & Roossin, P. S. (1990) A Statistical Approach to Machine Translation. Computational Linguistics 16, 79-85. Church, K. W. & Mercer, R. L. (1993) Introduction to the Special Issue on Computational Linguistics Using Large Corpora. Computational Linguistics 19, 1-24. Charniak, E. (1993) Statistical Language Learning. MIT Press. Hindle, D & Rooth, M. (1993) Structural Ambiguity and Lexical Relations. Computational Linguistics 19, 103-121. Krenn, B. & Samuelsson, C. (1995) The Linguist's Guide to Statistics. Universitt des Saarlandes: Computerlinguistik. Merialdo, B. (1994) Tagging English Text with a Probabilistic Model. Computational Linguistics 21, 165-201. Stolcke, A. (1995) An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities. Computational Linguistics 21, 165-201. Yarowsky, D. (1992) Word Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora. In Proceedings of the 14th International Conference on Computational Linguistics, Nantes, France, 454-460. Joakim Nivre Department of Linguistics Gteborg University S-412 98 Gteborg Sweden Email: joakimling.gu.seMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue