Learning from negative examples in set-expansion

Prateek Jindal, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the task of set-expansion on free text. Set-expansion has been viewed as a problem of generating an extensive list of instances of a concept of interest, given a few examples of the concept as input. Our key contribution is that we show that the concept definition can be significantly improved by specifying some negative examples in the input, along with the positive examples. The state-of-the art centroid-based approach to set-expansion doesn't readily admit the negative examples. We develop an inference-based approach to set-expansion which naturally allows for negative examples and show that it performs significantly better than a strong baseline.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages1110-1115
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
Country/TerritoryCanada
CityVancouver, BC
Period12/11/1112/14/11

ASJC Scopus subject areas

  • Engineering(all)

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