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Anaphora Resolution pp 345–368Cite as

Extracting Anaphoric Agreement Properties from Corpora

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Abstract

Anaphora resolution algorithms have long made use of the reliable agreement between pronouns and their antecedents in properties such as gender and number. To apply constraints or preferences for anaphoric agreement, real systems need ways to automatically determine these properties for arbitrary noun phrases, in context. This chapter describes a variety of algorithms for extracting noun gender and number, ranging from simple heuristics to large-scale machine learning approaches. We describe the drawbacks and advantages of the different algorithms, focusing mostly on English anaphora resolution. We pay special attention to recent methods for extracting agreement information directly from large volumes of raw text.

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Notes

  1. 1.

    See chapter “Linguistic and Cognitive Evidence About Anaphora” of this book for further discussion on the role of agreement information in human anaphora interpretation.

  2. 2.

    http://bllip.cs.brown.edu/download/emPronoun.tar.gz

  3. 3.

    Their model’s complexity only allowed training on a very small fraction of the total number of articles in Wikipedia. It would be interesting to assess the feasibility of using the resolution-to-article-topic heuristic on its own to learn a gender/number model from all of Wikipedia.

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Authors and Affiliations

  1. Department of Computer Science, University of Saskatchewan, JHU Center of Excellence, Baltimore, Saskatoon, SK, Canada, S7N 5C9

    Shane Bergsma

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  1. Shane Bergsma
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Correspondence to Shane Bergsma .

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  1. Trento, Italy

    Massimo Poesio

  2. Frankfurt am Main, Germany

    Roland Stuckardt

  3. Heidelberg, Germany

    Yannick Versley

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Bergsma, S. (2016). Extracting Anaphoric Agreement Properties from Corpora. In: Poesio, M., Stuckardt, R., Versley, Y. (eds) Anaphora Resolution. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47909-4_12

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  • DOI: https://doi.org/10.1007/978-3-662-47909-4_12

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