The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Resolving pronominal reference to abstract entities
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Combining sample selection and error-driven pruning for machine learning of coreference rules
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Kernel-based pronoun resolution with structured syntactic knowledge
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
A twin-candidate model for learning-based anaphora resolution
Computational Linguistics
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
A twin-candidate based approach for event pronoun resolution using composite kernel
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A twin-candidate model of coreference resolution with non-anaphor identification capability
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Improve tree kernel-based event pronoun resolution with competitive information
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Random walks down the mention graphs for event coreference resolution
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Event Anaphora Resolution is an important task for cascaded event template extraction and other NLP study. Previous study only touched on event pronoun resolution. In this paper, we provide the first systematic study to resolve event noun phrases to their verbal mentions crossing long distances. Our study shows various lexical, syntactic and positional features are needed for event noun phrase resolution and most of them, such as morphology relation, synonym and etc, are different from those features used for conventional noun phrase resolution. Syntactic structural information in the parse tree modeled with tree kernel is combined with the above diverse flat features using a composite kernel, which shows more than 10% F-score improvement over the flat features baseline. In addition, we employed a twin-candidate based model to capture the pair-wise candidate preference knowledge, which further demonstrates a statistically significant improvement. All the above contributes to an encouraging performance of 61.36% F-score on OntoNotes corpus.