An algorithm for pronominal anaphora resolution
Computational Linguistics
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
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Using the web to overcome data sparseness
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Bootstrapping path-based pronoun resolution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Glen, Glenda or Glendale: unsupervised and semi-supervised learning of English noun gender
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
An expectation maximization approach to pronoun resolution
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Enhancing multi-lingual information extraction via cross-media inference and fusion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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
Using query patterns to learn the duration of events
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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We present a novel approach to learning gender and number information for anaphora resolution Noun-pronoun pair counts are collected from gender-indicating lexico-syntactic patterns in parsed corpora, and occurrences of noun-pronoun pairs are mined online from the web Gender probabilities gathered from these templates provide features for machine learning Both parsed corpus and web-based features allow for accurate prediction of the gender of a given noun phrase Together they constructively combine for 96% accuracy when estimating gender on a list of noun tokens, better than any of our human participants achieved We show that using this gender information in simple or knowledge-rich pronoun resolution systems significantly improves performance over traditional gender constraints Our novel gender strategy would benefit any of the current top-performing coreference resolution systems.