Co-STAR: a co-training style algorithm for hyponymy relation acquisition from structured and unstructured text

Authors:
Jong-Hoon Oh;Ichiro Yamada;Kentaro Torisawa;Stijn De Saeger
Affiliations:
National Institute of Information and Communications Technology (NICT);National Institute of Information and Communications Technology (NICT);National Institute of Information and Communications Technology (NICT);National Institute of Information and Communications Technology (NICT)
Venue:
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Year:
2010

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Abstract

This paper proposes a co-training style algorithm called Co-STAR that acquires hyponymy relations simultaneously from structured and unstructured text. In Co-STAR, two independent processes for hyponymy relation acquisition -- one handling structured text and the other handling unstructured text -- collaborate by repeatedly exchanging the knowledge they acquired about hyponymy relations. Unlike conventional co-training, the two processes in Co-STAR are applied to different source texts and training data. We show the effectiveness of this algorithm through experiments on large-scale hyponymy-relation acquisition from Japanese Wikipedia and Web texts. We also show that Co-STAR is robust against noisy training data.