We describe a method for automatic word sense disambiguation using a text corpus and a
machinereadable dictionary (MRD). The method is based on word similarity and context …

We describe a method for automatic word sense disambiguation using a text corpus and a
machine-readable dictionary (MRD). The method is based on word similarity and context …

Most previous corpus-based algorithms disambiguate a word with a classifier trained from
previous usages of the same word. Separate classifiers have to be trained for different …

This paper presents a novel approach for word sense disambiguation. The underlying
algorithm has two main components:(1) pattern learning from available sense-tagged …

Word sense disambiguation has been recognized as a major problem in natural language
processing research for over forty years. Both quantitive and qualitative methods have been …

Word sense disambiguation algorithms, with few exceptions, have made use of only one
lexical knowledge source. We describe a system which performs unrestricted word sense …

This paper presents context-group discrimination, a disambiguation algorithm based on
clustering. Senses are interpreted as groups (or clusters) of similar contexts of the …

We present a corpus-based approach to word-sense disambiguation that only requires
information that can be automatically extracted from untagged text. We use unsupervised …

A word sense disambiguator that is able to distinguish among the many senses of common
words that are found in general-purpose, broad-coverage lexicons would be useful. For …

Graeme Hirst University of Toronto Of the many kinds of ambiguity in language, the two that
have received the most attention in computational linguistics are those of word senses and …