Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

TL;DR

This paper explores combining knowledge-based and corpus-based approaches to improve word sense disambiguation by integrating multiple sources of semantic information, supported by extensive experiments.

Contribution

It introduces a novel framework for combining different WSD methods and knowledge sources, enhancing disambiguation accuracy over individual approaches.

Findings

Combined methods outperform single-method approaches.

Multiple knowledge sources improve disambiguation accuracy.

Experimental results validate the effectiveness of the combined approach.

Abstract

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.