Exploiting background knowledge for relation extraction

Yee Seng Chan, Dan Roth

Research output: Contribution to conferencePaper

Abstract

Relation extraction is the task of recognizing semantic relations among entities. Given a particular sentence supervised approaches to Relation Extraction employed feature or kernel functions which usually have a single sentence in their scope. The overall aim of this paper is to propose methods for using knowledge and resources that are external to the target sentence, as a way to improve relation extraction. We demonstrate this by exploiting background knowledge such as relationships among the target relations, as well as by considering how target relations relate to some existing knowledge resources. Our methods are general and we suggest that some of them could be applied to other NLP tasks.

Original languageEnglish (US)
Pages152-160
Number of pages9
StatePublished - Dec 1 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: Aug 23 2010Aug 27 2010

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CountryChina
CityBeijing
Period8/23/108/27/10

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Exploiting background knowledge for relation extraction'. Together they form a unique fingerprint.

  • Cite this

    Chan, Y. S., & Roth, D. (2010). Exploiting background knowledge for relation extraction. 152-160. Paper presented at 23rd International Conference on Computational Linguistics, Coling 2010, Beijing, China.