Discovering relations among named entities from large corpora

Takaaki Hasegawa, Satoshi Sekine, Ralph Grishman

Research output: Contribution to journalConference articlepeer-review


Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. Our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.

Original languageEnglish (US)
Pages (from-to)415-422
Number of pages8
JournalProceedings of the Annual Meeting of the Association for Computational Linguistics
StatePublished - 2004
Event42nd Annual Meeting of the Association for Computational Linguistics, ACL 2004 - Barcelona, Spain
Duration: Jul 21 2004Jul 26 2004

ASJC Scopus subject areas

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


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