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Learning Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation

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Abstract

Knowledge of noun phrase anaphoricity might be profitably exploited in coreference resolution to bypass the resolution of non-anaphoric noun phrases. However, it is surprising to notice that recent attempts to incorporate automatically acquired anaphoricity information into coreference resolution systems have been far from expectation. This paper proposes a global learning method in determining the anaphoricity of noun phrases via a label propagation algorithm to improve learning-based coreference resolution. In order to eliminate the huge computational burden in the label propagation algorithm, we employ the weighted support vectors as the critical instances to represent all the anaphoricity-labeled NP instances in the training texts. In addition, two kinds of kernels, i.e., the feature-based RBF (Radial Basis Function) kernel and the convolution tree kernel with approximate matching, are explored to compute the anaphoricity similarity between two noun phrases. Experiments on the ACE2003 corpus demonstrate the great effectiveness of our method in anaphoricity determination of noun phrases and its application in learning-based coreference resolution.

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Authors and Affiliations

  1. NLP Lab, School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Guo-Dong Zhou (Senior Member, CCF, Member, ACM, IEEE) & Fang Kong (Member, CCF)

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  1. Guo-Dong Zhou
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  2. Fang Kong
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Correspondence to Guo-Dong Zhou.

Additional information

Supported by the National Natural Science Foundation of China under Grant Nos. 60873150, 90920004 and 61003153.

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Zhou, GD., Kong, F. Learning Noun Phrase Anaphoricity in Coreference Resolution via Label Propagation. J. Comput. Sci. Technol. 26, 34–44 (2011). https://doi.org/10.1007/s11390-011-9413-x

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