An algorithm for pronominal anaphora resolution
Computational Linguistics
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Probabilistic and rule-based tagger of an inflective language: a comparison
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Unsupervised word sense disambiguation rivaling supervised methods
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
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Text and knowledge mining for coreference resolution
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Minimally supervised induction of grammatical gender
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Offline strategies for online question answering: answering questions before they are asked
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
An expectation maximization approach to pronoun resolution
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Automatic acquisition of gender information for anaphora resolution
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
A search engine approach to estimating temporal changes in gender orientation of first names
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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English pronouns like he and they reliably reflect the gender and number of the entities to which they refer. Pronoun resolution systems can use this fact to filter noun candidates that do not agree with the pronoun gender. Indeed, broad-coverage models of noun gender have proved to be the most important source of world knowledge in automatic pronoun resolution systems. Previous approaches predict gender by counting the co-occurrence of nouns with pronouns of each gender class. While this provides useful statistics for frequent nouns, many infrequent nouns cannot be classified using this method. Rather than using co-occurrence information directly, we use it to automatically annotate training examples for a large-scale discriminative gender model. Our model collectively classifies all occurrences of a noun in a document using a wide variety of contextual, morphological, and categorical gender features. By leveraging large volumes of un-labeled data, our full semi-supervised system reduces error by 50% over the existing state-of-the-art in gender classification.