Co-STAR: A Co-training Style Algorithm for Hyponymy Relation Acquisition from Structured and Unstructured Text

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.

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