Comparison of Classification and Ranking Approaches to Pronominal Anaphora Resolution in Czech
Giang Linh Nguy, Zdeněk Žabokrtský and Václav Novák
SIGDIAL Workshop on Discourse and Dialogue (SIGDIAL 2009)
Queen Mary University of London, September 11-12, 2009
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.