D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation
This addresses a computational linguistics problem for natural language processing, but it is incremental as it adapts an existing optimization method to a known task.
The paper tackles word sense disambiguation by proposing D-Bees, a novel algorithm inspired by bee colony optimization, and finds that it performs comparably to ant colony optimization techniques on a standard dataset.
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.