An algorithm for pronominal anaphora resolution
Computational Linguistics
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Design and enhanced evaluation of a robust anaphor resolution algorithm
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
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
CogNIAC: high precision coreference with limited knowledge and linguistic resources
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
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Despite some promising early approaches, neural networks have by now received comparatively little attention as a machine learning model for robust, corpus-based anaphor resolution. The work presented in this paper is intended to fill the apparent gap in research. Based on a hybrid algorithm that combines manually knowledge-engineered antecedent filtering rules with machine-learned preference criteria, it is investigated what can be achieved by employing backpropagation networks for the corpus-based acquisition of preference strategies for pronoun resolution. Thorough evaluation will be carried out, thus systematically addressing the numerous experimental degrees of freedom, among which are sources of evidence (features, feature vector signatures), training data generation settings, number of hidden layer nodes, and number of training epochs. According to the evaluation results, the neural network approach performs at least similar to a decision-tree-based ancestor system that employs the same general hybrid strategy.