Automatic labeling of semantic roles
Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Evaluating and combining approaches to selectional preference acquisition
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Semantic classes and syntactic ambiguity
HLT '93 Proceedings of the workshop on Human Language Technology
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Towards robust semantic role labeling
Computational Linguistics
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Improving semantic role classification with Selectional Preferences
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter 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
Improving the use of pseudo-words for evaluating selectional preferences
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A flexible, corpus-driven model of regular and inverse selectional preferences
Computational Linguistics
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Modelling selectional preferences in a lexical hierarchy
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Statistical metaphor processing
Computational Linguistics
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This paper explores methods to alleviate the effect of lexical sparseness in the classification of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classification. The best results are obtained with a novel second-order distributional similarity measure, and the positive effect is specially relevant for out-of-domain data. Our findings suggest that selectional preferences have potential for improving a full system for Semantic Role Labeling.