Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Coreference for NLP applications
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Improving pronoun resolution by incorporating coreferential information of candidates
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
A ranking approach to pronoun resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Jointly labeling multiple sequences: a factorial HMM approach
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Simple coreference resolution with rich syntactic and semantic features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Coreference resolution in a modular, entity-centered model
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic acquisition of gender information for anaphora resolution
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Using regression for spectral estimation of HMMs
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents. Thus an observed pronoun can find its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns. A system implementing this model is evaluated on the ACE corpus with promising performance.