Co-star: A co-training style algorithm for hyponymy relation acquisition from structured and unstructured text
|Co-star: A co-training style algorithm for hyponymy relation acquisition from structured and unstructured text|
|Author(s)||Oh J.-H., Yamada I., Torisawa K., Saeger S.D.|
|Published in||Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Co-training, Hyponymy relation, Source text, Structured text, Training data, Wikipedia, Algorithms, Computational linguistics, Knowledge management, Stars, User interfaces, Natural language processing systems)|
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Co-star: A co-training style algorithm for hyponymy relation acquisition from structured and unstructured text is a 2010 conference paper written in English by Oh J.-H., Yamada I., Torisawa K., Saeger S.D. and published in Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference.
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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