Induction of semantic classes from natural language text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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 probabilistic account of logical metonymy
Computational Linguistics
The Journal of Machine Learning Research
Inducing a semantically annotated lexicon via EM-based clustering
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Introduction to Information Retrieval
Introduction to Information Retrieval
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Sense-based interpretation of logical metonymy using a statistical method
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
A latent dirichlet allocation method for selectional preferences
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Latent variable models of selectional preference
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 7: Argument selection and coercion
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
A mixture model with sharing for lexical semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Open domain event extraction from twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Local and global context for supervised and unsupervised metonymy resolution
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
Metonymic language is a pervasive phenomenon. Metonymic type shifting, or argument type coercion, results in a selectional restriction violation where the argument's semantic class differs from the class the predicate expects. In this paper we present an un-supervised method that learns the selectional restriction of arguments and enables the detection of argument coercion. This method also generates an enhanced probabilistic resolution of logical metonymies. The experimental results indicate substantial improvements the detection of coercions and the ranking of metonymic interpretations.