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Automatic Word Sense Discrimination (1998)
| Venue: | Journal of Computational Linguistics |
| Citations: | 536 - 1 self |
BibTeX
@ARTICLE{Schütze98automaticword,
author = {Hinrich Schütze},
title = {Automatic Word Sense Discrimination},
journal = {Journal of Computational Linguistics},
year = {1998},
volume = {24},
pages = {97--123}
}
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Abstract
This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training insta,nces or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words
Keyphrases
automatic word sense discrimination ambiguous word word space context-group discrimination sense cluster similar context labeled training insta good performance second-order co-occurrence semantic similarity external knowledge source disambiguation algorithm training corpus similar word closeness corresponds artificial ambiguous word real-valued space
