Word association norms, mutual information, and lexicography
Computational Linguistics
Selection and information: a class-based approach to lexical relationships
Selection and information: a class-based approach to lexical relationships
Introduction to the special issue on computational linguistics using large corpora
Computational Linguistics - Special issue on using large corpora: I
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
The selection of the most probable dependency structure in Japanese using mutual information
ACL '96 Proceedings of the 34th 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
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This paper proposes a new class-based method to estimate the strength of association in word co-occurrence for the purpose of structural disambiguation. To deal with sparseness of data, we use a conceptual dictionary as the source for acquiring upper classes of the words related in the co-occurrence, and then use t-scores to determine a pair of classes to be employed for calculating the strength of association. We have applied our method to determining dependency relations in Japanese and prepositional phrase attachments in English. The experimental results show that the method is sound, effective and useful in resolving structural ambiguities.