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2011
ACM

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

8 years 6 months ago
From actors, politicians, to CEOs: domain adaptation of relational extractors using a latent relational mapping
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 weaklysupervised relation extraction method. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Search and Retrieval General Terms Algorithms Keywords Relation Extraction, Domain Adaptation, Web Mining
Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuk
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where WWW
Authors Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka
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