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

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka
• Keywords: Internet Technology, Relation Extraction, Relation Extraction Method, Relation Types, WWW 2011