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Evaluating Hybrid Versus Data-Driven Coreference Resolution

  • Iris Hendrickx
  • Veronique Hoste
  • Walter Daelemans
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4410)

Abstract

In this paper, we present a systematic evaluation of a hybrid approach of combined rule-based filtering and machine learning to Dutch coreference resolution. Through the application of a selection of linguistically-motivated negative and positive filters, which we apply in isolation and combined, we study the effect of these filters on precision and recall using two different learning techniques: memory-based learning and maximum entropy modeling. Our results show that by using the hybrid approach, we can reduce up to 92 % of the training material without performance loss. We also show that the filters improve the overall precision of the classifiers leading to higher F-scores on the test set.

Keywords

Noun Phrase Positive Instance Negative Instance Maximum Entropy Modeling Negative Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Iris Hendrickx
    • 1
  • Veronique Hoste
    • 2
  • Walter Daelemans
    • 1
  1. 1.University of Antwerp, CNTS - Language Technology Group, Universiteitsplein 1, AntwerpBelgium
  2. 2.University College Ghent, LT3 - Language and Translation Technology Team 

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