Knowledge of the anaphoricity of a noun phrase might be profitably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. Perhaps surprisingly, recent attempts to incorporate automatically acquired anaphoricity information into coreference systems, however, have led to the degradation in resolution performance. This paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. In particular, we present a new corpus-based approach to anaphoricity determination. Experiments on three standard coreference data sets demonstrate the effectiveness of our approach.
REF further examined the representation and optimization issues in using anaphoricity information to improve the performance of coreference resolution.
Learning Noun Phrase Anaphoricity To Improve Conference Resolution: Issues In Representation And Optimization
Annual Meeting Of The Association For Computational Linguistics
Computer Science