Co-related verb argument selectional preferences

Hiram Calvo, Kentaro Inui, Yuji Matsumoto

研究成果: Conference contribution

抄録

Learning Selectional Preferences has been approached as a verb and argument problem, or at most as a tri-nary relationship between subject, verb and object. The correlation of all arguments in a sentence, however, has not been extensively studied for sentence plausibility measuring because of the increased number of potential combinations and data sparseness. We propose a unified model for machine learning using SVM (Support Vector Machines) with features based on topic-projected words from a PLSI (Probabilistic Latent Semantic Indexing) Model and PMI (Pointwise Mutual Information) as co-occurrence features, and WordNet top concept projected words as semantic classes. We perform tests using a pseudo-disambiguation task. We found that considering all arguments in a sentence improves the correct identification of plausible sentences with an increase of 10% in recall among other things.

本文言語English
ホスト出版物のタイトルComputational Linguistics and Intelligent Text Processing - 12th International Conference, CICLing 2011, Proceedings
編集者Alexander Gelbukh
出版社Springer Verlag
ページ133-143
ページ数11
ISBN(印刷版)9783642193996
DOI
出版ステータスPublished - 2011
イベント12th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2011 - Tokyo, Japan
継続期間: 2011 2 202011 2 26

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6608 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference12th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2011
国/地域Japan
CityTokyo
Period11/2/2011/2/26

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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