---
title: "A Ranking Approach to Pronoun Resolution"
authors:
  - "Pascal Denis"
  - "Jason Baldridge"
doi: ""
url: "https://ijcai.org/proceedings/2007/256"
pdf: "https://ijcai.org/proceedings/2007/0256.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1588-1592"
track: ""
keywords:
---

# A Ranking Approach to Pronoun Resolution

Pascal Denis, Jason Baldridge

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1588-1592.

## Abstract

We propose a supervised maximum entropy ranking approach to pronoun resolution as an alternative to commonly used classification-based approaches. Classification approaches consider only one or two candidate antecedents for a pronoun at a time, whereas ranking allows all candidates to be evaluated together. We argue that this provides a more natural fit for the task than classification and show that it delivers significant performance improvements on the ACE datasets. In particular, our ranker obtains an error reduction of 9.7% over the best classification approach, the twin-candidate model. Furthermore, we show that the ranker offers some computational advantage over the twin-candidate classifier, since it easily allows the inclusion of more candidate antecedents during training. This approach leads to a further error reduction of 5.4% (a total reduction of 14.6% over the twin-candidate model).