D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

TL;DR

The paper introduces D-Bees, a new bee-inspired algorithm for word sense disambiguation, demonstrating competitive performance against established optimization methods on a standard dataset.

Contribution

It presents a novel bee colony optimization-based algorithm specifically designed for WSD, expanding the application of BCO in natural language processing.

Findings

D-Bees performs comparably to ACO methods on WSD tasks.

The algorithm effectively finds sense sequences that maximize semantic relatedness.

Experimental results validate the approach on SemEval 2007 dataset.

Abstract

Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO)where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus)and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par.