Annotation and verification of sense pools in OntoNotes

  • Authors:

  • Liang-Chih Yu;Chung-Hsien Wu;Ru-Yng Chang;Chao-Hong Liu;Eduard Hovy

  • Affiliations:

  • Department of Information Management, Yuan-Ze University, No. 135, Yuan-Tung Road, Chung-Li 32030, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC;Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA 90292, United States

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2010

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Abstract

The paper describes the OntoNotes, a multilingual (English, Chinese and Arabic) corpus with large-scale semantic annotations, including predicate-argument structure, word senses, ontology linking, and coreference. The underlying semantic model of OntoNotes involves word senses that are grouped into so-called sense pools, i.e., sets of near-synonymous senses of words. Such information is useful for many applications, including query expansion for information retrieval (IR) systems, (near-)duplicate detection for text summarization systems, and alternative word selection for writing support systems. Although a sense pool provides a set of near-synonymous senses of words, there is still no knowledge about whether two words in a pool are interchangeable in practical use. Therefore, this paper devises an unsupervised algorithm that incorporates Google n-grams and a statistical test to determine whether a word in a pool can be substituted by other words in the same pool. The n-gram features are used to measure the degree of context mismatch for a substitution. The statistical test is then applied to determine whether the substitution is adequate based on the degree of mismatch. The proposed method is compared with a supervised method, namely Linear Discriminant Analysis (LDA). Experimental results show that the proposed unsupervised method can achieve comparable performance with the supervised method.