Automatic thesaurus construction using Bayesian networks
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Automatic Detection of Thesaurus relations for Information Retrieval Applications
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
Heuristics-Based Replenishment of Collocation Databases
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
Compilation of a Spanish Representative Corpus
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic extraction of semantic relations from specialized corpora
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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This work presents the results of the application of a technique for automatic extraction of semantic relations among words from a corpus. The technique used is the one proposed by Grefenstette in [1]. We brought contributions to the syntactic context notion in [1], aiming to improve the identification of semantically related words. Then, we carried on three different experiments using a Portuguese language corpus: the first one compares the original Grefenstette's technique with the technique modified with our contributions, the second experiment investigates which syntactic relation is more relevant when identifying semantic relations, and the last experiment investigates the influence of the parser errors on the quality of the extracted semantic relations. Results and their analyses are detailed in this article.