Large-scale verb entailment acquisition from the web

Authors:
Chikara Hashimoto;Kentaro Torisawa;Kow Kuroda;Stijn De Saeger;Masaki Murata;Jun'ichi Kazama
Affiliations:
National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan
Venue:
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Year:
2009

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Abstract

Textual entailment recognition plays a fundamental role in tasks that require indepth natural language understanding. In order to use entailment recognition technologies for real-world applications, a large-scale entailment knowledge base is indispensable. This paper proposes a conditional probability based directional similarity measure to acquire verb entailment pairs on a large scale. We targeted 52,562 verb types that were derived from 108 Japanese Web documents, without regard for whether they were used in daily life or only in specific fields. In an evaluation of the top 20,000 verb entailment pairs acquired by previous methods and ours, we found that our similarity measure outperformed the previous ones. Our method also worked well for the top 100,000 results.