An improved extraction pattern representation model for automatic IE pattern acquisition

Kiyoshi Sudo, Satoshi Sekine, Ralph Grishman

Research output: Contribution to journalConference articlepeer-review

Abstract

Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improvement in recall using Subtree patterns.

Original languageEnglish (US)
JournalProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume2003-July
StatePublished - 2003
Event41st Annual Meeting of the Association for Computational Linguistics, ACL 2003 - Sapporo, Japan
Duration: Jul 7 2003Jul 12 2003

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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