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

ACL ID C10-1095
Title Co-STAR: A Co-training Style Algorithm for Hyponymy Relation Acquisition from Structured and Unstructured Text
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

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 hy- ponymy relation acquisition – one han- dling structured text and the other han- dling unstructured text – collaborate by re- peatedly exchanging the knowledge they acquired about hyponymy relations. Un- like conventional co-training, the two pro- cesses in Co-STAR are applied to dif- ferent source texts and training data. We show the effectiveness of this al- gorithm 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.