Comparison of classification and ranking approaches to pronominal anaphora resolution in Czech

Abstract

In this paper we compare two Machine Learning approaches to the task of pronominal anaphora resolution: a conventional classification system based on C5.0 decision trees, and a novel perceptron-based ranker. We use coreference links annotated in the Prague Dependency Treebank 2.0 for training and evaluation purposes. The perceptron system achieves f-score 79.43% on recognizing coreference of personal and possessive pronouns, which clearly outperforms the classifier and which is the best result reported on this data set so far. © 2009 Association for Computational Linguistics.