Experiments in automatic statistical thesaurus construction
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic thesaurus construction using Bayesian networks
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Corpus processing for lexical acquisition
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
On learning more appropriate Selectional Restrictions
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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
Selection Restrictions Acquisition from Corpora
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Assessment of Selection Restrictions Acquisition
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Directional distributional similarity for lexical inference
Natural Language Engineering
An approach to acquire word translations from non-parallel texts
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Communications of the ACM
Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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This paper explores different strategies for extracting similarity relations between words from partially parsed text corpora. The strategies we have analysed do not require supervised training nor semantic information available from general lexical resources. They differ in the amount and the quality of the syntactic contexts against which words are compared. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts.