Similarity-based Word Sense Disambiguation

Karov, Yael and Edelman, Shimon (1996) Similarity-based Word Sense Disambiguation. [Preprint]

Full text available as:

[img] Postscript


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 similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method can learn even from very sparse training data, achieving over 92% correct disambiguation performance.

Item Type:Preprint
Subjects:Psychology > Cognitive Psychology
ID Code:569
Deposited By: Edelman, Shimon
Deposited On:17 Nov 1997
Last Modified:11 Mar 2011 08:54


Repository Staff Only: item control page