A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
SemEval-2010 task 1: Coreference resolution in multiple languages
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
The Uppsala-FBK systems at WMT 2011
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
UBIU for multilingual coreference resolution in OntoNotes
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
Phrase detectives: Utilizing collective intelligence for internet-scale language resource creation
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special section on internet-scale human problem solving and regular papers
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BART (Versley et al., 2008) is a highly modular toolkit for coreference resolution that supports state-of-the-art statistical approaches and enables efficient feature engineering. For the SemEval task 1 on Coreference Resolution, BART runs have been submitted for German, English, and Italian. BART relies on a maximum entropy-based classifier for pairs of mentions. A novel entity-mention approach based on Semantic Trees is at the moment only supported for English.