A machine learning approach to coreference resolution of noun phrases

  • Authors:

  • Wee Meng Soon;Hwee Tou Ng;Daniel Chung Yong Lim

  • Affiliations:

  • DSO National Laboratories;DSO National Laboratories;DSO National Laboratories

  • Venue:
  • Computational Linguistics - Special issue on computational anaphora resolution
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of "organization," "person," or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, in-dicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.