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Colloquium - Singer

"80 and tea: ho May I hell Pooh?": Efficient Algorithms for Multi-Class Multi-Label Text Categorization
AT&T Machine Learning Group
3/19/1998
3:45pm-4:45pm

When an AT&T customer dials 00, she gets an operator who asks: "AT&T: how may I help you?". A possible answer might be: "Ah yes, I would like to call 908-508-1464 and charge it to my Visa." Being able to automatically determine what the caller actually wants (in this case a "dial-for-me" request and a "credit-card-call" request) is a difficult task especially since there might be more than one action to be taken due to the caller's request.

This work focuses on algorithms which learn from examples to perform text and speech categorization tasks. We first show how to extend the standard notion of classification by allowing each instance to be associated with multiple labels. We then discuss our approach for text classification which is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text classification tasks. We present results comparing BoosTexter and a number of other text classification algorithms. We conclude with a demonstration of the system for the "AT&T: how may I help you?" speech categorization task.

This talk is based on joint work with Robert Schapier of AT&T Labs.

Refreshments will be served immediately before the talk at 3:30pm.

Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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