LearningWeb query patterns for imitatingWikipedia articles

Shohei Tanaka, Naokaki Okazaki, Mitsuru Ishizuka

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

1 Citation (Scopus)

Abstract

This paper presents a novel method for acquiring a set of query patterns to retrieve documents containing important information about an entity. Given an existing Wikipedia category that contains the target entity, we extract and select a small set of query patterns by presuming that formulating search queries with these patterns optimizes the overall precision and coverage of the returned Web information. We model this optimization problem as a weighted maximum satisfiability (weighted Max-SAT) problem. The experimental results demonstrate that the proposed method outperforms other methods based on statistical measures such as frequency and point-wise mutual information (PMI), which are widely used in relation extraction.

Original languageEnglish
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages1229-1237
Number of pages9
Volume2
Publication statusPublished - 2010
Externally publishedYes
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing
Duration: 2010 Aug 232010 Aug 27

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CityBeijing
Period10/8/2310/8/27

ASJC Scopus subject areas

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

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  • Cite this

    Tanaka, S., Okazaki, N., & Ishizuka, M. (2010). LearningWeb query patterns for imitatingWikipedia articles. In Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 1229-1237)