LearningWeb query patterns for imitatingWikipedia articles

Shohei Tanaka*, Naokaki Okazaki, Mitsuru Ishizuka

*Corresponding author for this work

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

2 Citations (Scopus)


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
Number of pages9
Publication statusPublished - 2010
Externally publishedYes
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing
Duration: 2010 Aug 232010 Aug 27


Other23rd International Conference on Computational Linguistics, Coling 2010

ASJC Scopus subject areas

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


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