Multi-view clustering with web and linguistic features for relation extraction

Yulan Yan, Haibo Li, Yutaka Matsuo, Zhenglu Yang, Mitsuru Ishizuka

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

Abstract

Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Webbased methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view coclustering approach for semantic relation extraction. One is feature clustering by automatically learning clustering functions for Web features, linguistic features simultaneously based on a subset of entity pairs. The other is relation clustering, using the feature clustering functions to define learning function for relation extraction. Our experiments demonstrate the superiority of our clustering approach comparing with several state-of-theart clustering methods.

Original languageEnglish
Title of host publicationAdvances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010
Pages140-146
Number of pages7
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event12th International Asia Pacific Web Conference, APWeb 2010 - Busan
Duration: 2010 Apr 62010 Apr 8

Other

Other12th International Asia Pacific Web Conference, APWeb 2010
CityBusan
Period10/4/610/4/8

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Yan, Y., Li, H., Matsuo, Y., Yang, Z., & Ishizuka, M. (2010). Multi-view clustering with web and linguistic features for relation extraction. In Advances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010 (pp. 140-146). [5474142] https://doi.org/10.1109/APWeb.2010.64