A hybrid genetic-ant colony optimization algorithm for the word sense disambiguation problem

Abstract Word sense disambiguation (WSD) is a natural language processing problem that occurs at the semantic level. It consists of determining the sense of a polysemous word that is suitable in a particular context. WSD has been addressed using several approaches, including metaheuristic algorithms. We propose hybrid algorithms for WSD that consist of a self-adaptive genetic algorithm (SAGA) and variants of ant colony optimization (ACO) algorithms: max-min ant system (MMAS) and ant colony system (ACS). SAGA is used to automatically tune the parameters of MMAS and ACS. The ACO algorithms are adapted based on a combination of semantic relatedness between sequences of senses corresponding to the context words and semantic relatedness between the sense of a target word and the sense of a context word. We evaluated the performance of the two ACO algorithms (MMASWSD and ACSWSD) and their hybridization with SAGA (GMMASWSD and GACSWSD) on fine-grained and coarse-grained corpora, and compared them with the best-performing algorithms. The empirical results indicate that GMMASWSD outperformed the other variants and all of the rival algorithms on the fine-grained corpora. However, GMMASWSD did not achieve the best performance on the coarse-grained corpus, even though its performance was close to that of the best algorithm.