A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th 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
Unrestricted Coreference: Identifying Entities and Events in OntoNotes
ICSC '07 Proceedings of the International Conference on Semantic Computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Semantic role labeling for coreference resolution
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
CoNLL-2011 shared task: modeling unrestricted coreference in OntoNotes
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Data-driven multilingual coreference resolution using resolver stacking
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Using syntactic dependencies to solve coreferences
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Simple maximum entropy models for multilingual coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Hybrid rule-based algorithm for coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
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In this paper, we describe a coreference solver based on the extensive use of lexical features and features extracted from dependency graphs of the sentences. The solver uses Soon et al. (2001)'s classical resolution algorithm based on a pairwise classification of the mentions. We applied this solver to the closed track of the CoNLL 2011 shared task (Pradhan et al., 2011). We carried out a systematic optimization of the feature set using cross-validation that led us to retain 24 features. Using this set, we reached a MUC score of 58.61 on the test set of the shared task. We analyzed the impact of the features on the development set and we show the importance of lexicalization as well as of properties related to dependency links in coreference resolution.