| Assignment/Tutor | ||||||||||||
|
A bulletin board has also been created for the class, which willi be monitored by the TAs.
| Required | C. Manning & H. Schuetze, Foundations of Statistical Natural Language Processing, MIT, 1999. | Errata |
| for which there is an on-line edition from MIT CogNet | ||
| Optional | D. Jurafsky & J. Martin, Speech and Language Processing, Prentice Hall, 2nd ed., 2008. | Errata |
| Recommended | A. Martelli, Python in a Nutshell, 2nd ed., O'Reilly, 2006. | Errata |
| Optional | M. Lutz, Learning Python, 3rd ed., O'Reilly, 2007. | Errata |
| Free! | various tutorials on the Python website |
| Topic | Title | Author | Publication Details |
| parsing,
phrase structure models |
Statistical Language Learning | E. Charniak | MIT Press, 1993. |
| machine learning | The Elements of Statistical Learning | T. Hastie, R. Tibshirani and J. Friedman | Springer, 2001. |
| information theory
(including entropy) |
Elements of Information Theory | T. M. Cover and J. A. Thomas | Wiley & Sons, 1991. |
| maximum entropy modelling | A Maximum Entropy Approach to Natural Language Processing | A. L. Berger, S. A. Della Pietra and V. J. Della Pietra | Computational Linguistics, 22(1): 39-71. |
| hidden Markov models
(state emission) |
Fundamentals of Speech Recognition, Chapter 6. | L. Rabiner and B.-H. Juang | Prentice Hall, 1993. |
| Good-Turing estimation | A comparison of the enhanced Good-Turing and deleted estimation methods for estimating probabilities of English bigrams | K. Church and W. Gale | Computer Speech and Language 5:19-54. |
| information retrieval | Modern Information Retrieval | R. Baeza-Yates and B. Ribeiro-Neto | ACM Press, 1999. |
| text summarization | Automatic Summarization | I. Mani | Benjamins, 2001. |
| phonetics (articulatory and acoustic) | Acoustic Phonetics | K. N. Stevens | MIT Press, 1998. |
| Date | Event |
| Mon, 4 January | First lecture |
| Fri, 15 January | Last day to add course (CSC 2511) |
| Sun, 10 January | Last day to add course (CSC 401) |
| Fri, 5 February | Assignment 1 due |
| 15-19 February | Reading Week - no classes |
| Fri, 26 February | Last day to drop course (CSC 2511) |
| Sun, 7 March | Last day to drop course (CSC 401) |
| Fri, 5 March | Assignment 2 due |
| Mon, 29 March | Last lecture |
| Thu, 1 April | Assignment 3 due |
| 7-23 April | Final exam period |
| Assignment 1 | 20% |
| Assignment 2 | 20% |
| Assignment 3 | 20% |
| Final | 40% |
Important note on final: A mark of at least a D- on the final exam is required to pass the course. In other words, if you receive an F on the final exam you automatically fail the course, regardless of your performance on homeworks.
Important note on homeworks: No late homeworks will be accepted except in case of documented medical or other emergencies.
Policy on collaboration: No collaboration on homeworks is permitted. The work you submit must be your own. No student is permitted to discuss the final exam with any other student until the instructor or TAs make the solutions publicly available. Failure to observe this policy is an academic offense, carrying a penalty ranging from a zero on the homework to suspension from the university.
To view these handouts you will need access to a PDF viewer. If your machine does not have the required software, you can download Adobe Acrobat Reader for free.
Gerald Penn, 8 February,
2010
This web-page was adapted from the web-page for another course,
created by Vassos Hadzilacos.