Understanding the Value of Features for Coreference Resolution

In recent years there has been substantial work on the important problem of coreference resolution, most of which has concentrated on the development of new models and algorithmic techniques. These works often show that complex models improve over a weak pairwise baseline. However, less attention has been given to the importance of selecting strong features to support learning a coreference model. This paper describes a rather simple pairwise classification model for coreference resolution, developed with a well-designed set of features. We show that this produces a state-of-the-art system that outperforms systems built with complex models. We suggest that our system can be used as a baseline for the development of more complex models

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Eric Bengtson, Dan Roth
• Keywords: Complex Models, Coreference Resolution, EMNLP 2008, Natural Language Processing, Weak Pairwise Baseline