Description and Objectives

Welcome to Advanced NLP!  This year we will focus on 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, topic identification, and visualization of the results of those methods.

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, unsupervised topic identification, and visualization methods, including text summarization
• build a foundation of techniques for approximate Bayesian reasoning for unsupervised text analysis
• obtain experience with techniques for evaluating and visualizing the results of unsupervised learning processes

In addition to learning the concepts and statistical methods, this course aims to help the student build real tools, to prepare for careers in the field, and to jump into NLP-related research.

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Instructor:  Eric Ringger

Semester: Winter 2009

Office Hours: M 3-4pm; W 4-5pm; and by appointment

Lecture: MWF 12:00-12:50pm, 3718 HBLL

Syllabus:  Link

Schedule: Link

Wiki: Link

Text: Research papers and a selected chapter or two from related texts -- see Schedule

Announcements:  See the BYU Blackboard page for this course.  Please check for announcements regularly.

Grades: On Blackboard

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Previous instance of Dr. Ringger's CS 601R course : Winter 2008 Syllabus and Schedule