An experiment on learning appropriate Selectional Restrictions from a parsed corpus

Authors:
Francesc Ribas Framis
Affiliations:
Universitat Politècnica de Catalunya, Barcelona, Spain
Venue:
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Year:
1994

Citing 6
Cited 6

Word association norms, mutual information, and lexicography

Computational Linguistics
Accurate methods for the statistics of surprise and coincidence

Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank

Computational Linguistics - Special issue on using large corpora: II
Computational lexicons: the neat examples and the odd exemplars

ANLC '92 Proceedings of the third conference on Applied natural language processing
Empirical study of predictive powers of simple attachment schemes for post-modifier prepositional phrases

ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora

COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2

Generalizing case frames using a thesaurus and the MDL principle

Computational Linguistics
Clustering verbs semantically according to their alternation behaviour

COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
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
Acquiring word-meaning mappings for natural language interfaces

Journal of Artificial Intelligence Research
Automatic acquisition of attribute host by selectional constraint resolution

MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Unsupervised learning of verb argument structures

CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a methodology to extract Selectional Restrictions at a variable level of abstraction from phrasally analyzed corpora. The method relays in the use of a wide-coverage noun taxonomy and a statistical measure of the co-occurrence of linguistic items. Some experimental results about the performance of the method are provided.