CS601R Main Page
From NLPWiki
Contents |
CS 601R, Section 001, Winter 2008
Advanced Natural Language Processing: Text Classification, Text Clustering, and Topic Identification
Description and Objectives
Welcome to Advanced NLP! A conceivable and reasonable alternative title for the course is "Text Mining". The field of text mining has attracted significant interest in recent years as enormous collections of text data have become available across the web, behind firewalls on corporate networks, and on our own PCs. One side of the problem is information retrieval, epitomized by web search. Another side of the problem is the selective extraction of structured nuggets of information from unstructured text. This course focuses on a third aspect of the problem: exploratory data analysis in large collections of text, with particular emphasis on techniques for text classification, text clustering, and topic identification.
The learning objectives for the course are as follows:
- acquire experience conducting exploratory data analysis on large collections of text
- gain in-depth experience with and understanding of approaches to document classification, feature engineering, feature selection, sentiment classification, document clustering, and unsupervised topic identification
- build a foundation of techniques for approximate Bayesian reasoning for unsupervised text analysis
- obtain experience with techniques for evaluating the results of unsupervised learning processes
In addition to learning the concepts and techniques of statistical NLP, this course aims to help the student build real tools, to prepare for careers in the field, and to jump into NLP research.
Course Links
Instructor: Dr. Eric Ringger
Lecture location: 241 MSRB
Lecture time: MWF 9:00-9:50pm
Weekly Schedule (including instructor and TA hours)
Text: Research papers and a selected chapter or two from related texts -- see the Schedule
Announcements: See the BYU BlackBoard page for this course. Please check for announcements regularly.
Grades: On BYU BlackBoard
Project Guidelines
How To: Technical Details
Use the links in this section to get up and running with the programming assignments. Check back often, as inactive links will shortly lead to useful content.
About Data
About Code
Questions and Answers
If you have a question, check the FAQ first, in case it has already been asked by another student and answered:
