Fine-Grained Word Sense Disambiguation Based on Parallel Corpora, Word Alignment, Word Clustering and Aligned Wordnets

TL;DR

This paper introduces a novel word sense disambiguation approach leveraging parallel corpora, word alignment, clustering, and aligned wordnets, achieving promising results and aiding in error detection in multilingual wordnet alignments.

Contribution

It presents a new method combining parallel corpora, word alignment, clustering, and aligned wordnets for improved word sense disambiguation and error detection.

Findings

Achieved encouraging disambiguation accuracy

System can identify alignment errors in multilingual wordnets

Method integrates multiple linguistic resources effectively

Abstract

The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. The wordnets are aligned to the Princeton Wordnet, according to the principles established by EuroWordNet. The evaluation of the WSD system, implementing the method described herein showed very encouraging results. The same system used in a validation mode, can be used to check and spot alignment errors in multilingually aligned wordnets as BalkaNet and EuroWordNet.