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Enhancing Preference-Based Anaphora Resolution with Genetic Algorithms

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Natural Language Processing — NLP 2000 (NLP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1835))

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Abstract

The paper argues that a promising way to improve the success rate of preference-based anaphora resolution algorithms is the use of machine learning. The paper outlines MARS - a program for automatic resolution of pronominal anaphors and describes an experiment which we have conducted to optimise the success rate of MARS with the help of a genetic algorithm. After the optimisation we noted an improvement up to 8% for some files. The results obtained after optimisation are discussed.

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References

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Author information

Authors and Affiliations

  1. School of Humanities, Languages and Social Sciences, University of Wolverhampton, Stafford Street, Wolverhampton, WV11SB, UK

    Constantin OrĂsan, Richard Evans & Ruslan Mitkov

Authors
  1. Constantin OrĂsan
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  2. Richard Evans
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  3. Ruslan Mitkov
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Editor information

Editors and Affiliations

  1. Computer Engineering Department and Computer Technology Institute, University of Patras, 26500, Patras, Greece

    Dimitris N. Christodoulakis

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© 2000 Springer-Verlag Berlin Heidelberg

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OrĂsan, C., Evans, R., Mitkov, R. (2000). Enhancing Preference-Based Anaphora Resolution with Genetic Algorithms. In: Christodoulakis, D.N. (eds) Natural Language Processing — NLP 2000. NLP 2000. Lecture Notes in Computer Science(), vol 1835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45154-4_17

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  • DOI: https://doi.org/10.1007/3-540-45154-4_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67605-8

  • Online ISBN: 978-3-540-45154-9

  • eBook Packages: Springer Book Archive

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