Natural Language Processing Prof. Jason Eisner Course # 600.465 - Fall 2006 Announcements | Course Information | Schedule

Announcements

• 10/22/06 HW3 is available and weighs in at 19 pages. This is the hardest homework -- the one you'll look back on proudly at your 5th reunion and say "I climbed Mount NLP!" It is due on Monday, 11/13.
• 10/22/06 Remember that tomorrow, Monday, is the in-class midterm.
• 9/27/06 HW2 is available and weighs in at 20 pages. It is due on Friday, 10/13.
• 9/13/06 The online version of HW1 has been updated to match the hardcopy version handed out in class yesterday. It is due on Monday, 9/25 - please get this one in on time.
• 9/11/06 Today, Monday, is the first day of class (2pm MTW in Shaffer 301).
• 9/11/06 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, Shaffer 301
Prof:	Jason Eisner - ()
TA:	Omar Zaidan - (ozaidan at cs dot jhu dot edu)
CA:	Asheesh Laroia - (asheesh at asheesh dot org) Constantinos Michael - (stvorph at gmail dot com)
Office hrs:	For Prof: MT 3-4pm, or by appt, in NEB 324A For TA: T 4-5:30, W 12:15-1:45, in undergrad lab (NEB 225)
Mailing list:	... 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 Cheating: here's what it means Intellectual engagement: much encouraged Announcements: Read mailing list and this page!
Related course sites:	List of many courses Bob Berwick (MIT) Steven Bird (Penn) Jan Hajic (JHU) Alon Lavie (CMU) Lillian Lee (Cornell) Chris Manning (Stanford) Jim Martin (Colorado) Philip Resnik (Maryland) Ellen Riloff (Utah)

[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.

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: 2006/10/24 13:47:37 $ (GMT)