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Various applications in computational linguistics and artificial intelligencerely on high-performing word sense disambiguation techniques to solvechallenging tasks such as information retrieval, machine translation, questionanswering, and document clustering. While text comprehension is intuitive forhumans, machines face tremendous challenges in processing and interpreting ahuman's natural language. This paper presents a novel knowledge-based wordsense disambiguation algorithm, namely Sequential Contextual Similarity MatrixMultiplication (SCSMM). The SCSMM algorithm combines semantic similarity,heuristic knowledge, and document context to respectively exploit the merits oflocal context between consecutive terms, human knowledge about terms, and adocument's main topic in disambiguating terms. Unlike other algorithms, theSCSMM algorithm guarantees the capture of the maximum sentence context whilemaintaining the terms' order within the sentence. The proposed algorithmoutperformed all other algorithms when disambiguating nouns on the combinedgold standard datasets, while demonstrating comparable results to currentstate-of-the-art word sense disambiguation systems when dealing with eachdataset separately. Furthermore, the paper discusses the impact of granularitylevel, ambiguity rate, sentence size, and part of speech distribution on theperformance of the proposed algorithm.

A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph