A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A corpus-based investigation of definite description use
Computational Linguistics
Sequence Ontology annotation guide: Conference Papers
Comparative and Functional Genomics
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The influence of minimum edit distance on reference resolution
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Active learning for HPSG parse selection
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Bootstrapping and evaluating named entity recognition in the biomedical domain
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Statistical anaphora resolution in biomedical texts
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Corpus design for biomedical natural language processing
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Active learning for coreference resolution
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Active learning for coreference resolution
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
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In this paper we present our experiments with active learning to improve the performance of our probabilistic anaphora resolution system. We have adopted entropy-based uncertainty measures to select new instances to be added to our training data. The actively selected instances, however, were not more successful in improving the performance of the system than the same amount of randomly selected instances. The uncertainty measures we used behave differently from each other when selecting new instances, but none of them achieved remarkable performance. Further studies on active sample selection for anaphora resolution are necessary.