JSM 2005 Online Program

Abstract #302599

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	298
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	IMS
Abstract - #302599
Title:	Unlabeled Data in Statistical Language Processing
Author(s):	David D. Lewis*+
Companies:	David D. Lewis Consulting & Ornarose, Inc.
Address:	858 W. Armitage Ave., #296, Chicago, IL, 60614,
Keywords:	unsupervised ; semisupervised ; bootstrapping ; document classification ; information retrieval ; computational linguistics
Abstract:	Statistical approaches, particularly supervised learning methods, are the dominant paradigm in computer processing of natural language text and speech. Manually annotating data with desired machine outputs, and tuning parameters to that data, often leads to cheaper and more effective systems than directly encoding linguistic intuitions. However, annotating data is a nontrivial cost, and there have been many attempts to leverage unlabeled or partially labeled data to reduce this expense. I will review some of the major approaches to using unlabeled data in language processing tasks, including pseudofeedback, transduction, colearning, and active learning. While sometimes providing considerable benefits, the effectiveness of these approaches varies unpredictably across tasks. Most of these techniques lack a formal statistical footing, so foundational work might lead to both better understanding and practical improvements.

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