Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Recovering implicit information
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
Event-building through role-filling and anaphora resolution
ACL '91 Proceedings of the 29th 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
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
An NP-cluster based approach to coreference resolution
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
SemEval-2010 task 10: linking events and their participants in discourse
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Discriminative approach to predicate-argument structure analysis with zero-anaphora resolution
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Probabilistic frame-semantic parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Beyond NomBank: a study of implicit arguments for nominal predicates
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 10: Linking events and their participants in discourse
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SEMAFOR: Frame argument resolution with log-linear models
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
VENSES++: Adapting a deep semantic processing system to the identification of null instantiations
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
End-to-end coreference resolution via hypergraph partitioning
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A cross-lingual ILP solution to zero anaphora resolution
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Semi-supervised frame-semantic parsing for unknown predicates
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Narrowing the modeling gap: a cluster-ranking approach to coreference resolution
Journal of Artificial Intelligence Research
Desperately seeking implicit arguments in text
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
Unrestricted coreference resolution via global hypergraph partitioning
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
LIARc: labeling implicit ARguments in spanish deverbal nominalizations
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Linking implicit semantic roles is a challenging problem in discourse processing. Unlike prior work inspired by SRL, we cast this problem as an anaphora resolution task and embed it in an entity-based coreference resolution (CR) architecture. Our experiments clearly show that CR-oriented features yield strongest performance exceeding a strong baseline. We address the problem of data sparsity by applying heuristic labeling techniques, guided by the anaphoric nature of the phenomenon. We achieve performance beyond state-of-the art.