An algorithm for pronominal anaphora resolution
Computational Linguistics
Making large-scale support vector machine learning practical
Advances in kernel methods
An empirically based system for processing definite descriptions
Computational Linguistics
A corpus-based evaluation of centering and pronoun resolution
Computational Linguistics - Special issue on computational anaphora resolution
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Never look back: an alternative to centering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A centering approach to pronouns
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic processing of large corpora for the resolution of anaphora references
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Corpus-based identification of non-anaphoric noun phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improving pronoun resolution by incorporating coreferential information of candidates
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Specialized models and ranking for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A ranking approach to pronoun resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Proceedings of the third international workshop on Data and text mining in bioinformatics
Comparison of classification and ranking approaches to pronominal anaphora resolution in Czech
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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A recently-proposed machine learning approach to reference resolution -- the twin-candidate approach -- has been shown to be more pormising than the traditional single-candidate approach. This paper presents a pronoun interpretation system that extends the twin-candidate framework by (1) equippmg it with the ability to identify non-referential pronouns. (2) training different models for handling different types of pronouns, and (3) incorporating linguistic knowledge sources that are generally not employed in traditional pronoun resolvers. The resulting system, when evaluated on a standard coreference corpus, outpreforms not only the original twin-candidate approach but also a state-of-the-art pronoun resolver.