Corpus-Based Anaphora Resolution towards Antecedent Preference

In this paper we propose a corpus-based approach to anaphora resolution combining a machine learning method and statistical information. First, a decision tree trained on an annotated corpus determines the coreference relation of a given anaphor and antecedent candidates and is utilized as a filter in order to reduce the num- ber of potential candidates. In the second step, preference selection is achieved by taking into account the frequency infor- mation of coreferential and non-referentiai pairs tagged in the training corpus as well as distance features within the current dis- course. Preliminary experiments concerning the resolution of Japanese pronouns in spoken-language dialogs result in a success rate of 80.6%