A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
CogNIAC: high precision coreference with limited knowledge and linguistic resources
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
Understanding the value of features for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Dependency-driven anaphoricity determination for coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Stanford's multi-pass sieve coreference resolution system at the CoNLL-2011 shared task
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
A machine learning-based coreference detection system for OntoNotes
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Coreference resolution system using maximum entropy classifier
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
CoNLL-2012 shared task: Modeling Multilingual Unrestricted Coreference in OntoNotes
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
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This paper describes a coreference resolution system for CONLL 2012 shared task developed by HLT_HITSZ group, which incorporates rule-based and statistic-based techniques. The system performs coreference resolution through the mention pair classification and linking. For each detected mention pairs in the text, a Decision Tree (DT) based binary classifier is applied to determine whether they form a coreference. This classifier incorporates 51 and 61 selected features for English and Chinese, respectively. Meanwhile, a rule-based classifier is applied to recognize some specific types of coreference, especially the ones with long distances. The outputs of these two classifiers are merged. Next, the recognized coreferences are linked to generate the final coreference chain. This system is evaluated on English and Chinese sides (Closed Track), respectively. It achieves 0.5861 and 0.6003 F1 score on the development data of English and Chinese, respectively. As for the test dataset, the achieved F1 scores are 0.5749 and 0.6508, respectively. This encouraging performance shows the effectiveness of our proposed coreference resolution system.