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

Danushka Bollegala; Yutaka Matsuo; Mitsuru Ishizuka

doi:10.1145/1963192.1963200

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

Danushka Bollegala^*, Yutaka Matsuo, Mitsuru Ishizuka

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Pages	13-14
Number of pages	2
DOIs	https://doi.org/10.1145/1963192.1963200
Publication status	Published - 2011
Externally published	Yes
Event	20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad Duration: 2011 Mar 28 → 2011 Apr 1

Other

Other	20th International Conference Companion on World Wide Web, WWW 2011
City	Hyderabad
Period	11/3/28 → 11/4/1

Keywords

domain adaptation
relation extraction
web mining

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

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10.1145/1963192.1963200

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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, 20th International Conference Companion on World Wide Web, WWW 2011, Hyderabad, 11/3/28. https://doi.org/10.1145/1963192.1963200

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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.",

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AB - 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.

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BT - Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011

Y2 - 28 March 2011 through 1 April 2011

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

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Keywords

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