A systematic comparison of various statistical alignment models
Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Possessive pronominal anaphor resolution in Portuguese written texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Inducing multilingual POS taggers and NP bracketers via robust projection across aligned corpora
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Natural Language Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The Mitkov Algorithm for Anaphora Resolution in Portuguese
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
Learning Coreference Resolution for Portuguese Texts
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
A Machine Learning Approach to Portuguese Pronoun Resolution
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Graph-cut-based anaphoricity determination for coreference resolution
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Deeper Look into Features for Coreference Resolution
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
Cross-lingual annotation projection of semantic roles
Journal of Artificial Intelligence Research
Supervised noun phrase coreference research: the first fifteen years
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Coreference resolution with reconcile
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
AnCora-CO: Coreferentially annotated corpora for Spanish and Catalan
Language Resources and Evaluation
Translation-based projection for multilingual coreference resolution
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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The majority of current coreference resolution systems rely on annotated corpora to train classifiers for this task. However, this is possible only for languages for which annotated corpora are available. This paper presents a system that automatically extracts coreference chains from texts in Portuguese without the need for Portuguese corpora manually annotated with coreferential information. To achieve this, an English coreference resolver is run on the English part of an English-Portuguese parallel corpus. The coreference pairs identified by the resolver are projected to the Portuguese part of the corpus using automatic word alignment. These projected pairs are then used to train the coreference resolver for Portuguese. Evaluation of the system reveals that it does not outperform a head match baseline. This is due to the fact that most of the projected pairs have the same head, which is learnt by the Portuguese classifier. This suggests that a more accurate English coreference resolver is necessary. A better projection algorithm is also likely to improve the performance of the system.