CIS 8590: Topics in Computer Science -- Text Mining and Language Processing

CIS 8590: Topics in Computer Science -- Text Mining and Language Processing [Spring 2008]

Prerequisites, Text, Description, Grading, Exams, Final Project.

Additional information about this course may be found on the Web at
http://knight.cis.temple.edu/~yates/cis8590/.

Lecture Time: Thursdays: 4:40pm to 7:10pm in Tuttleman 1B

Instructor : Alexander Yates

Office : Wachman Hall, Room 303A
E-Mail :
Contact Hours: Thursdays from 2:00pm to 4:00pm, or by appointment, or drop by and see if I'm in.

Miscellaneous:

Each student should have a general Temple email address, usually of the form firstName.LastName@temple.edu
Important student information is accessible from http://owlnet.temple.edu/
The last day to drop from the course (and get tuition refund) is Monday, September 15, 2008. The last day to withdraw from the course (no refund) is Monday, November 3, 2008. Students who have previously withdrawn from this course, or who have already withdrawn from 5 courses since September 2003 may not withdraw.
Any student who has a need for accomodation based on the impact of a disability should contact me privately to discuss the specific situation as soon as possible. Students with documented disabilities should contact Disability Resources and Services at 215-204-1280 in 100 Ritter Hall to coordinate reasonable accomodations.
Freedom to teach and freedom to learn are inseparable facets of academic freedom. The University has adopted a policy on Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) which can be accessed through the following link: http://policies.temple.edu/getdoc.asp?policy_no=03.70.02
Students should be familiar with the University statement on academic honesty found at the following link http://www.temple.edu/bulletin/Responsibilities_rights/responsibilities/responsibilities.shtm
The grievance procedures are available online

PREREQUISITES

A general familiarity and basic level of comfort with probability and statistics is essential, and will be assumed. Any of the following courses, or specific permission of the instructor, should be enough: CIS 8525, 8526, 8527, 9603, 9664.

TEXT

There is no textbook for this course. We will be reading extensively from the research literature.

DESCRIPTION

This course will give a broad overview of problems and techniques in natural language processing, and then move on to cover the latest research in selected topics. The overview part of the course will cover problems in:

Information Retrieval: Building indexes, data compression, representation of queries and documents, and similarity functions.
Information Extraction: Building hierarchies of knowledge (ontologies), determining the meaning of words, and determining the relationships that exist between entities referred to in text.

The in-depth part of the course will focus on the latest research in unsupervised information extraction. This part of the course will cover such techniques as stemming, pointwise mutual information, pattern-matching, bootstrapping, TF-IDF, n-gram models, Hidden Markov Models, Conditional Random Fields, statistical parsing, clustering, and language modeling.

GRADING

Quizzes: 30%
In-class Participation: 20%
Final Project: 50%

EXAMS AND QUIZZES

All exams and quizzes are closed book. Their content is cumulative, i.e. they address the material from the entire semester up to the day of the exam. If a student misses the midterm for an emergency [as agreed with instructor], there will be no makeup exam: the homeworks, quizzes, and final project will become proportionally more important. If a student misses the midterm without previous agreement and without definitive proof as to the medical or legal reasons, he or she will get a zero for that exam. Quizzes that are missed will not be made up. //The final exam is mandatory on the scheduled day.

FINAL PROJECT

Several project ideas will be suggested during the course of the semester, but students are free to suggest their own, especially if they relate to their current research. Students will be expected to come up with innovative, novel solutions to problems in text mining and language processing.

Course projects will be undertaken individually or in small teams (2-3 students). Each student on a team will receive the same grade for the project; it is up to the team members to divide the work fairly. More information on course projects will be provided soon.