Using graph based method to improve bootstrapping relation extraction

Haibo Li, Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

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

9 Citations (Scopus)

Abstract

Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages127-138
Number of pages12
Volume6609 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011 - Tokyo
Duration: 2011 Feb 202011 Feb 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6609 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011
CityTokyo
Period11/2/2011/2/26

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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