Distant supervision for relation extraction using tree kernels

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

1 Citation (Scopus)

Abstract

In this paper we define a simple Relation Extraction system based on SVMs using tree kernels and employing a weakly supervised approach, known as Distant Supervision (DS). Our method uses the simple one-versus-all strategy to handle overlapping relations, i.e., de- fined on the same pair of entities. The DS data is defined over the New York Times corpus by means of Freebase as an external knowledge base, which indicates the relations of some of the entities of the NYT text. Our experiments show that our simple approach performs well in this domain with respect to the current state of the art.

Original languageEnglish
Title of host publicationCEUR Workshop Proceedings
PublisherCEUR-WS
Volume1404
Publication statusPublished - 2015
Event6th Italian Information Retrieval Workshop, IIR 2015 - Cagliari, Italy
Duration: 25 May 201526 May 2015

Other

Other6th Italian Information Retrieval Workshop, IIR 2015
CountryItaly
CityCagliari
Period25/5/1526/5/15

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Keywords

  • Distant Supervision
  • Relation Extraction
  • Support Vector Machines
  • Tree Kernels

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Abad, A., & Moschitti, A. (2015). Distant supervision for relation extraction using tree kernels. In CEUR Workshop Proceedings (Vol. 1404). CEUR-WS.