Making large-scale support vector machine learning practical
Advances in kernel methods
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Two uses of anaphora resolution in summarization
Information Processing and Management: an International Journal
Unrestricted Coreference: Identifying Entities and Events in OntoNotes
ICSC '07 Proceedings of the International Conference on Semantic Computing
Incorporating rule-based and statistic-based techniques for coreference resolution
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
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In this paper, we present our supervised learning approach to coreference resolution in ConLL corpus. The system relies on a maximum entropy-based classifier for pairs of mentions, and adopts a rich linguisitically motivated feature set, which mostly has been introduced by Soon et al (2001), and experiment with alternaive resolution process, preprocessing tools, and classifiers. We optimize the system's performance for MUC (Vilain et al, 1995), BCUB (Bagga and Baldwin, 1998) and CEAF (Luo, 2005).