C4.5: programs for machine learning
C4.5: programs for machine learning
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
An architecture for anaphora resolution
ANLC '88 Proceedings of the second conference on Applied natural language processing
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Never look back: an alternative to centering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Evaluating a focus-based approach to anaphora resolution
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Anaphora resolution: a multi-strategy approach
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Anaphora resolution of Japanese zero pronouns with deictic reference
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
An algorithm for anaphora resolution in Spanish texts
Computational Linguistics - Special issue on computational anaphora resolution
A machine learning approach to pronoun resolution in spoken dialogue
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Integration of referential scope limitations into Japanese pronoun resolution
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Improving pronoun resolution by incorporating coreferential information of candidates
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Utilization of coreferences for the translation of utterances containing anaphoric expressions
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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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 number of potential candidates. In the second step, preference selection is achieved by taking into account the frequency information of coreferential and non-referential pairs tagged in the training corpus as well as distance features within the current discourse. Preliminary experiments concerning the resolution of Japanese pronouns in spoken-language dialogs result in a success rate of 80.6%.