Bilingual co-training for monolingual hyponymy-relation acquisition

Authors:
Jong-Hoon Oh;Kiyotaka Uchimoto;Kentaro Torisawa
Affiliations:
National Institute of Information and Communications Technology (NICT), Soraku-gun, Kyoto, Japan;National Institute of Information and Communications Technology (NICT), Soraku-gun, Kyoto, Japan;National Institute of Information and Communications Technology (NICT), Soraku-gun, Kyoto, Japan
Venue:
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Year:
2009

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Abstract

This paper proposes a novel framework called bilingual co-training for a large-scale, accurate acquisition method for monolingual semantic knowledge. In this framework, we combine the independent processes of monolingual semantic-knowledge acquisition for two languages using bilingual resources to boost performance. We apply this framework to large-scale hyponymy-relation acquisition from Wikipedia. Experimental results show that our approach improved the F-measure by 3.6--10.3%. We also show that bilingual co-training enables us to build classifiers for two languages in tandem with the same combined amount of data as required for training a single classifier in isolation while achieving superior performance.