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ACL
1994

Similarity-Based Estimation of Word Cooccurrence Probabilities

8 years 11 months ago
Similarity-Based Estimation of Word Cooccurrence Probabilities
In many applications of natural language processing it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations "eat a peach" and "eat a beach" is more likely. Statistical NLP methods determine the likelihood of a word combination according to its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in a given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on "most similar" words. We describe a probabilistic word association model based on distributional word similarity, and apply it to improving probability estimates for unseen word bigrams in a variant of Katz's back-off model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigram...
Ido Dagan, Fernando C. N. Pereira, Lillian Lee
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1994
Where ACL
Authors Ido Dagan, Fernando C. N. Pereira, Lillian Lee
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