A Proposal for Word Sense Disambiguation using Conceptual Distance

TL;DR

This paper introduces an automatic Word Sense Disambiguation method leveraging WordNet's noun taxonomy and a novel Conceptual Density measure, evaluated on the Brown Corpus without manual intervention.

Contribution

It proposes a fully automatic disambiguation approach based on conceptual distance, eliminating the need for hand coding, tagging, or training.

Findings

Effective disambiguation on the Brown Corpus

Automatic evaluation against SemCor shows promising results

No manual lexical entries or training required

Abstract

This paper presents a method for the resolution of lexical ambiguity and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiment have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus.