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

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
ページ13-14
ページ数2
DOI
出版ステータスPublished - 2011
外部発表はい
イベント20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad
継続期間: 2011 3 282011 4 1

Other

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

フィンガープリント 「From actors, politicians, to CEOs: Domain adaptation of relational extractors using a latent relational mapping」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル