Modeling the impact of shared visual information on collaborative reference
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Backbone extraction and pruning for speeding up a deep parser for dialogue systems
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Increasing the coverage of a domain independent dialogue lexicon with VerbNet
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Utilizing visual attention for cross-modal coreference interpretation
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
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This thesis describes research into ways of improving pronoun resolution algorithms. Many systems today perform well, as high as 80% accuracy in some cases over news article domains, but resolving the other 20% requires additional complex information that isn't usually available to systems. In this project we investigate whether syntactic ranking preferences, discourse structure, and lexical semantics can improve an existing pronoun resolution system to close the “20%” gap. Our results show that syntactic ranking preferences and lexical semantics can boost performance in newspaper and spoken dialogue domains.