A maximum entropy approach to natural language processing
Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving pronoun resolution using statistics-based semantic compatibility information
ACL '05 Proceedings of the 43rd 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
A ranking approach to pronoun resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Proceedings of the third international workshop on Data and text mining in bioinformatics
Polynomial to linear: efficient classification with conjunctive features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Exploring variations across biomedical subdomains
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Overview of the protein coreference task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
The taming of reconcile as a biomedical coreference resolver
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
What's in a name?: entity type variation across two biomedical subdomains
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Much effort in the research community has been spent on solving the anaphora resolution or pronoun resolution problem, and in particular for news texts. In order to selectively inherit the previous works and solve the same problem for a new domain, we carried out a comparative study with three different corpora: MUC, ACE for the news texts, and GENIA for bio-medical papers. Our corpus analysis and experimental results show the significant differences in the use of pronouns in the two domains, thus by properly considering the characteristics of a domain, we can improve the performance of pronoun resolution for that domain.