C4.5: programs for machine learning
C4.5: programs for machine learning
An algorithm for pronominal anaphora resolution
Computational Linguistics
Machine Learning
Evaluating Natural Language Processing Systems: An Analysis and Review
Evaluating Natural Language Processing Systems: An Analysis and Review
A New, Fully Automatic Version of Mitkov's Knowledge-Poor Pronoun Resolution Method
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Getting Useful Gender Statistics from English Text
Getting Useful Gender Statistics from English Text
A non-projective dependency parser
ANLC '97 Proceedings of the fifth 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
A centering approach to pronouns
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
An empirical investigation of the relation between discourse structure and co-reference
COLING '00 Proceedings of the 18th 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
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Empirical evaluations of animacy annotation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Extracting human Spanish nouns
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Language Resources and Evaluation
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In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.