Statistical anaphora resolution in biomedical texts

  • Authors:
  • Caroline Gasperin;Ted Briscoe

  • Affiliations:
  • University of Cambridge, Cambridge, UK;University of Cambridge, Cambridge, UK

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a probabilistic model for resolution of non-pronominal anaphora in biomedical texts. The model seeks to find the antecedents of anaphoric expressions, both coreferent and associative ones, and also to identify discourse-new expressions. We consider only the noun phrases referring to biomedical entities. The model reaches state-of-the art performance: 56--69% precision and 54--67% recall on coreferent cases, and reasonable performance on different classes of associative cases.