From actors, politicians, to CEOs: Domain adaptation of relational extractors using a latent relational mapping

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

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

Abstract

We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lower-dimensional projection between different relations, and learning a relational classifier for the target relation type with instance sampling. We evaluate the proposed method using a dataset that contains 2000 instances for 20 different relation types. Our experimental results show that the proposed method achieves a statistically significant macro-average F-score of 62.77. Moreover, the proposed method outperforms numerous baselines and a previously proposed weakly-supervised relation extraction method.

Original languageEnglish
Title of host publicationProceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Pages13-14
Number of pages2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad
Duration: 2011 Mar 282011 Apr 1

Other

Other20th International Conference Companion on World Wide Web, WWW 2011
CityHyderabad
Period11/3/2811/4/1

Keywords

  • domain adaptation
  • relation extraction
  • web mining

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). From actors, politicians, to CEOs: Domain adaptation of relational extractors using a latent relational mapping. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011 (pp. 13-14) https://doi.org/10.1145/1963192.1963200