An increasing fraction of the world's information and data is now represented in textual form. For example, the World Wide Web, online news feeds, and other Internet sources contain a tremendous volume of information. However, users seeking information do not have unlimited attention, and therefore methods of summarizing, clustering, categorizing and discovering patterns in the information space are required.

The goal of this workshop is to explore computer methods for automatically extracting information from text and hypertext sources. Examples might include systems that automatically extract descriptions of corporate mergers by monitoring online newsfeeds, or systems that automatically extract addresses and phone numbers from home pages on the web.

Interested participants are encouraged to submit workshop papers describing work in progress, that may not yet have reached the point where journal publication is waranted. Relevant topics include (but are not restricted to) computer methods for information extraction from text and hypertext, automated learning of such methods, automatic text summarization, and text classification. Papers will be distributed in advance of the workshop, and the workshop itself will be organized into brief presentations of papers, along with interactive discussions of key research themes.

Organizers:

• Yiming Yang (chair)
• Jaime Carbonell
• Steve Fienberg
• Tom Mitchell

Contact conald@cs.cmu.edu for more information