Paper: Combining Sample Selection And Error-Driven Pruning For Machine Learning Of Coreference Rules

ACL ID W02-1008
Title Combining Sample Selection And Error-Driven Pruning For Machine Learning Of Coreference Rules
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

Most machine learning solutions to noun phrase coreference resolution recast the problem as a classification task. We ex- amine three potential problems with this reformulation, namely, skewed class dis- tributions, the inclusion of “hard” training instances, and the loss of transitivity in- herent in the original coreference relation. We show how these problems can be han- dled via intelligent sample selection and error-driven pruning of classification rule- sets. The resulting system achieves an F- measure of 69.5 and 63.4 on the MUC- 6 and MUC-7 coreference resolution data sets, respectively, surpassing the perfor- mance of the best MUC-6 and MUC-7 coreference systems. In particular, the system outperforms the best-performing learning-based coreference system to date.