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In the field of natural language processing, the semantic disambiguation of words is beneficial to several applications, which helps us to identify the correct meaning of a word or a sequence of words according to the given context. It can be formulated as a combinatorial optimization problem where the goal is to find the set of meanings that contribute to improving the semantic relationship between target words. The Crow Search Algorithm (CSA) is a nature-inspired algorithm. It mimics the food foraging behavior of crow birds and their social interaction. CSA can deal with both continuous and discrete optimization problems. In this paper, the Word Sense Disambiguation (WSD) is modelled as a combinatorial optimization problem that is by nature a discrete problem. For this propose the discrete version of CSA has been adapted for solving the WSD problem and a DCSA-based WSD approach is proposed and called ADCSA-WSD. The proposed approach has been evaluated and compared with state-of-the-art approaches using three well-known benchmark datasets (SemCor 3.0, SensEval-02, SensEval-03). Experimental results show that ADCSA-WSD approach is performing better than other approaches.