Paper: Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution

ACL ID P09-1073
Title Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

This paper explores how to apply the notion of caching introduced by Walker (1996) to the task of zero-anaphora resolution. We propose a machine learning-based imple- mentation of a cache model to reduce the computational cost of identifying an an- tecedent. Our empirical evaluation with Japanese newspaper articles shows that the number of candidate antecedents for each zero-pronoun can be dramatically reduced while preserving the accuracy of resolving it.