Natural Language Processing Prof. Jason Eisner Course # 600.465 - Fall 2007 Announcements | Course Information | Schedule

Announcements

• 11/24/07 I've modified HW5 slightly to add a new dataset ("ic2") that you can use to test your smoothing code, if you like.
• 11/21/07 HW5 is available at 18 pages. It is due on December 10. (Remember that there is a hard deadline of December 16 even if you have > 6 late days left.)
• 11/19/07 While I still encourage you to hand in HW4 before the Thanksgiving break, and enjoy your break, you are welcome to take an extension till Mon. 11/26 at 2pm. (But the next homework will still be available on 11/21 as scheduled.)
• 11/14/07 HW4 is available. This is a relatively short homework due on Wed. 11/21 -- the day before Thanksgiving.
• 10/23/07 HW3 is available at 19 pages. This is the hardest assignment in the course, with three major challenges: probabilistic Earley parsing, making parsing efficient, and designing semantic attachments. It is due on Monday, 11/12, at 2pm.
• 9/24/07 HW2 is available and weighs in at 21 pages. It is due on Friday, 10/12, at 2pm.
• 9/11/07 HW1 (9 pages) is available. Usually you should download homework assignments yourself from this webpage, but I will hand this first one out in class. It is due on Monday, 9/24, at 2pm: please get this one in on time so we can discuss it in class on 9/24.
• 9/9/07 Tomorrow, Monday, is the first day of class (2pm MTW in Hodson 311).
• 9/9/07 Please bookmark this page (http://cs.jhu.edu/~jason/465).

Vital Statistics [1]

Welcome! This course is designed to introduce you to some of the problems and solutions of NLP, and their relation to linguistics and statistics. You need to know how to program (e.g., 600.120) and use common data structures (600.226). It might also be nice to have some previous familiarity with automata (600.271) and probabilities (550.310). At the end you should agree (I hope!) that language is subtle and interesting, feel some ownership over some of NLP's formal and statistical techniques, and be able to understand research papers in the field.

Course catalog entry: This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers? The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition and machine translation. There are a number of structured but challenging programming assignments. Prerequisite: 600.226. [Eisner, Applications, Fall] 3 credits

Lectures:	MTW 2-3 pm, Hodson 311
Prof:	Jason Eisner - ()
TA:	Jason Smith - ()
CA:	TBA
Office hrs:	For Prof: Mon/Tue 3-4pm, or by appt, in NEB 324A For TA: TBA
Mailing list:	probably ... public questions, discussion, announcements
Web page:	http://cs.jhu.edu/~jason/465
Textbook:	Jurafsky & Martin (required - online partial draft of next edition wants your comments!) Manning & Schütze (recommended - online PDF version is accessible for free from within JHU)
Policies:	Grading: homework 45%, participation 10%, midterm 15%, final 30% Submission: via this web form Lateness: floating late days policy Honesty: here's what it means Intellectual engagement: much encouraged Announcements: Read mailing list and this page!
Related course sites:	List of many courses - some may have useful material!

[1] Or are these statistics? What is the scope of "statistics" anyway? Does it just mean "information"?

Schedule

Warning: For future lectures and assignments, the links below take you to last year's versions, which are subject to change.

Warning: The Jurafsky & Martin chapter numbers refer to the 1st edition, so be careful about which chapters you download from the 2nd edition website.

Department of Computer Science
The Johns Hopkins University
New Engineering Building Room 224
3400 North Charles Street
Baltimore, MD 21218
(410) 516-8775 (phone)
(410) 516-6134 (fax)

Last Modification $Date: 2008/03/29 16:19:57 $ (GMT)