C4.5: programs for machine learning
C4.5: programs for machine learning
Centering: a framework for modeling the local coherence of discourse
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
Focusing for interpretation of pronouns
Computational Linguistics
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
A centering approach to pronouns
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Providing a unified account of definite noun phrases in discourse
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Corpus-based anaphora resolution towards antecedent preference
CorefApp '99 Proceedings of the Workshop on Coreference and its Applications
Kernel-based pronoun resolution with structured syntactic knowledge
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Coreference systems based on kernels methods
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Supervised ranking for pronoun resolution: some recent improvements
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Web-based annotation of anaphoric relations and lexical chains
LAW '07 Proceedings of the Linguistic Annotation Workshop
A Deeper Look into Features for Coreference Resolution
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
Employing the centering theory in pronoun resolution from the semantic perspective
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A twin-candidate based approach for event pronoun resolution using composite kernel
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A pronoun anaphora resolution system based on factorial hidden Markov models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Anaphora resolution for biomedical literature by exploiting multiple resources
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Improve tree kernel-based event pronoun resolution with competitive information
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Coreferential information of a candidate, such as the properties of its antecedents, is important for pronoun resolution because it reflects the salience of the candidate in the local discourse. Such information, however, is usually ignored in previous learning-based systems. In this paper we present a trainable model which incorporates coreferential information of candidates into pronoun resolution. Preliminary experiments show that our model will boost the resolution performance given the right antecedents of the candidates. We further discuss how to apply our model in real resolution where the antecedents of the candidate are found by a separate noun phrase resolution module. The experimental results show that our model still achieves better performance than the baseline.