<![CDATA[The cognitive effort required for humans to perceive similarities and relationships between words is considerable. Measuring similarity and relatedness between text components such as words, texts, or documents is challenging, and it continues to be an active area of research across various domains. The complexity of language and the diverse factors that influence similarity and relatedness make this task an ongoing research focus. Researchers are exploring diverse approaches, to improve the accuracy and effectiveness of measuring similarity and relatedness in text. The utilization of knowledge sources, such as WordNet, has been a popular approach for modeling semantic relationships between words. However, Recently, distributional semantic models, such as Word2Vec, have demonstrated their ability to effectively capture semantic information and outperform lexiconbased methods in terms of unidirectional contextual similarity outcomes. In contrast to lexicon-based approaches, which rely on structure, distributional models leverage context to capture semantics. This study proposes a novel approach that linearly combines the lexical databases WordNet and Word2Vec to measure semantic similarity, focusing on improving upon previous techniques. The proposed approach is thoroughly detailed and evaluated using popular datasets to determine its effectiveness. The experimental results indicate that the proposed approach achieves highly satisfactory results and surpasses the performance of individual methods.]]>
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