How to encode semantic knowledge: a method for meaning representation and computer-aided acquisition
Computational Linguistics
GPSM: a Generaized Probabilistic Semantic Model for ambiguity resolution
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Linguistic knowledge generator
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Verb sense disambiguation based on dual distributional similarity
Natural Language Engineering
Statistical sense disambiguation with relatively small corpora using dictionary definitions
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Evaluation of semantic clusters
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Similarity-based estimation of word cooccurrence probabilities
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Generalizing automatically generated selectional patterns
COLING '94 Proceedings of the 15th 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
New York University: description of the Proteus system as used for MUC-5
MUC5 '93 Proceedings of the 5th conference on Message understanding
(Almost) automatic semantic feature extraction from technical text
HLT '94 Proceedings of the workshop on Human Language Technology
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Frequency information on co-occurrence patterns can be automatically collected from a syntactically analyzed corpus; this information can then serve as the basis for selectional constraints when analyzing new text from the same domain. Better coverage of the domain can be obtained by appropriate generalization of the specific word patterns which are collected. We report here on an approach to automatically make suitable generalizations: using the co-occurrence data to compute a confusion matrix relating individual words, and then using the confusion matrix to smooth the original frequency data.