Publication at Faculty of Mathematics and Physics |
2009
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.