An algorithm for pronominal anaphora resolution
Computational Linguistics
A maximum entropy approach to natural language processing
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
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th 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
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Applying Co-Training to reference 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
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Automatic acquisition of gender information for anaphora resolution
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Global learning of noun phrase anaphoricity in coreference resolution via label propagation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
DAARC'07 Proceedings of the 6th discourse anaphora and anaphor resolution conference on Anaphora: analysis, algorithms and applications
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
The same-head heuristic for coreference
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Dependency-driven anaphoricity determination for coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Learning noun phrase anaphoricity in coreference resolution via label propagation
Journal of Computer Science and Technology - Special issue on natural language processing
Journal of Biomedical Informatics
NADA: a robust system for non-referential pronoun detection
DAARC'11 Proceedings of the 8th international conference on Anaphora Processing and Applications
Towards unsupervised learning of temporal relations between events
Journal of Artificial Intelligence Research
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We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a fixed list of potential antecedents for each pronoun. We show that unsupervised learning is possible in this context, as the performance of our system is comparable to supervised methods. Our results indicate that a probabilistic gender/number model, determined automatically from unlabeled text, is a powerful feature for this task.