C4.5: programs for machine learning
C4.5: programs for machine learning
Acquiring predicate-argument mapping information from multilingual texts
Corpus processing for lexical acquisition
Linguistic indicators for language understanding: using machine learning methods to combine corpus-based indicators for aspectual classification of clauses
From grammar to lexicon: unsupervised learning of lexical syntax
Computational Linguistics - Special issue on using large corpora: II
Using semantic preferences to identify verbal participation in role switching alternations
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic extraction of subcategorization from corpora
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic verb classification using distributions of grammatical features
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatic acquisition of a large subcategorization dictionary from corpora
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Automatic acquisition of the lexical semantics of verbs from sentence frames
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Role of word sense disambiguation in lexical acquisition: predicting semantics from syntactic cues
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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We automatically classify verbs into lexical semantic classes, based on distributions of indicators of verb alternations, extracted from a very large annotated corpus. We address a problem which is particularly difficult because the verb classes, although semantically different, show similar surface syntactic behavior. Five grammatical features are sufficient to reduce error rate by more than 50% over chance: we achieve almost 70% accuracy in a task whose baseline performance is 34%, and whose expert-based upper bound we calculated at 86.5%. We conclude that corpus-driven extraction of grammatical features is a promising methodology for find-grained verb classification.