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Chinese pronominal anaphora resolution using lexical knowledge and entropy-based weight.

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Title: Chinese pronominal anaphora resolution using lexical knowledge and entropy-based weight.
Authors: Wu, Dian-Song1, Liang, Tyne1
Source: Journal of the American Society for Information Science & Technology. Nov2008, Vol. 59 Issue 13, p2138-2145. 8p. 2 Diagrams, 9 Charts, 1 Graph.
Subjects: Anaphora (Linguistics), Chinese writing, Pronominals (Grammar), Comparative grammar, Linguistics, Artificial intelligence
Abstract: Pronominal anaphors are commonly observed in written texts. In this article, effective Chinese pronominal anaphora resolution is addressed by using lexical knowledge acquisition and salience measurement. The lexical knowledge acquisition is aimed to extract more semantic features, such as gender, number, and collocate compatibility by employing multiple resources. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The resolution is justified with a real corpus and compared with a rule-based model. Experimental results by five-fold cross-validation show that our approach yields 82.5% success rate on 1343 anaphoric instances. In comparison with a general rule-based approach, the performance is improved by 7%. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Dian-Song%22">Wu, Dian-Song</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liang%2C+Tyne%22">Liang, Tyne</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="DE" term="%22Anaphora+%28Linguistics%29%22">Anaphora (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+writing%22">Chinese writing</searchLink><br /><searchLink fieldCode="DE" term="%22Pronominals+%28Grammar%29%22">Pronominals (Grammar)</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+grammar%22">Comparative grammar</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
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  Data: Pronominal anaphors are commonly observed in written texts. In this article, effective Chinese pronominal anaphora resolution is addressed by using lexical knowledge acquisition and salience measurement. The lexical knowledge acquisition is aimed to extract more semantic features, such as gender, number, and collocate compatibility by employing multiple resources. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The resolution is justified with a real corpus and compared with a rule-based model. Experimental results by five-fold cross-validation show that our approach yields 82.5% success rate on 1343 anaphoric instances. In comparison with a general rule-based approach, the performance is improved by 7%. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.20922
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      – SubjectFull: Chinese writing
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      – SubjectFull: Pronominals (Grammar)
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      – SubjectFull: Linguistics
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      – TitleFull: Chinese pronominal anaphora resolution using lexical knowledge and entropy-based weight.
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              Text: Nov2008
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              Y: 2008
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