CS674 Natural Language Processing

CS674 Natural Language Processing

Spring 2005

Time: Mondays and Wednesdays, 11:15-12:05 Place: 216 Olin Hall Instructor: Claire Cardie, 5161 Upson Hall Office hours: Monday 1-2, Thursday 3-4
Course Materials: Lecture Notes, Readings, Assignments Critique and Project Information Course Management System (CMS): We'll be using the CS department course management system for submission of assignments, grading, etc. You can get to CMS via the above link. You'll need your Cornell netid and password. electronic submission instructions (or see page 19 of the .pdf version of the CMS instructions for students). Resources: Lillian Lee's list of general NLP resources NLP resources available locally are listed under the local resources link of the Cornell NLP home page.
Course Description
	This course presents a graduate-level introduction to natural language processing, the primary concern of which is the study of human language use from a computational perspective. The course covers syntactic analysis, semantic interpretation, and discourse processing, examining both symbolic and statistical approaches. Possible topics include information extraction, natural language generation, memory models, ambiguity resolution, finite-state methods, mildly context-sensitive formalisms, deductive approaches to interpretation, machine translation, and machine learning of natural language.
Topics to be Covered (tentative)
	Introduction to NLP History and state-of-the-art Lexical semantics and word-sense disambiguation Part-of-speech tagging and HMMs Morphology Context-sensitive spelling correction Noisy channel model Language modeling Parsing Discourse processing Dialogue systems Generation Inference and world knowledge Semantic analysis Information extraction Machine Translation
Reference Material
	The recommended text book for the course is: Daniel Jurafsky and James H. Martin, Speech and Language Processing, Prentice-Hall, 2000. Other useful references: Christopher Manning and Hinrich Schutze. Foundations of Statistical NLP, MIT Press, 1999. James Allen. Natural Language Understanding, 2nd edition. Eugene Charniak. Statistical Language Learning, MIT Press, 1996. Robert Dale, Hermann Moisl and Harold Somers, eds. Handbook of Natural Langauge Processing, 2000. Lucja M. Iwanska and Stuart C. Shapiro, eds. Natural Language Processing and Knowledge Representation, MIT Press, 2000. Frederick Jelinek. Statistical Methods for Speech Recognition, MIT Press, 1998. Roland R. Hausser. Foundations of Computational Linguistics: Human-Computer Communication in Natural Language, Springer Verlag, 2001.
Prerequisites
	Elementary computer science background, elementary knowledge of probability, familiarity with context-free grammars.
Grading
	20%: critiques of selected readings and research papers Guidelines for writing critiques 20%: presentations of selected readings and research papers 50%: final project. Grade based on (1) preliminary project proposal, (2) project literature survey, (3) project presentation, (4) final write-up. You are to complete an independent project on some topic in natural language processing. Both programming and non-programming projects are fine. All must include a careful write-up. 10%: participation You'll be expected to participate in class discussion and class exercises or otherwise demonstrate an interest in the material studied in the course.
Academic Integrity
	You are responsible for knowing and following Cornell's academic integrity policy. Absolute integrity is expected of every Cornell student in all academic undertakings; he/she must in no way misrepresent his/her work fraudulently or unfairly advance his/her academic status, or be a party to another student's failure to maintain academic integrity. The maintenance of an atmosphere of academic honor and the fulfillment of the provisions of this Code are the responsibilities of the students and faculty of Cornell University. Therefore, all students and faculty members shall refrain from any action that would violate the basic principles of this Code. Violation of the academic integrity policy will not be tolerated, and will result in an F in the course. See the University Code of Academic Integrity and the Department Policy on Academic Integrity.