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Similarity is a comparative-subjective measure that varies with the domainwithin which it is considered. In several NLP applications such as documentclassification, pattern recognition, chatbot question-answering, sentimentanalysis, etc., identifying an accurate similarity score for sentence pairs hasbecome a crucial area of research. In the existing models that assesssimilarity, the limitation of effectively computing this similarity based oncontextual comparisons, the localization due to the centering theory, and thelack of non-semantic textual comparisons have proven to be drawbacks. Hence,this paper presents a multi-layered semantic similarity network model builtupon multiple similarity measures that render an overall sentence similarityscore based on the principles of Network Science, neighboring weightedrelational edges, and a proposed extended node similarity computation formula.The proposed multi-layered network model was evaluated and tested againstestablished state-of-the-art models and is shown to have demonstrated betterperformance scores in assessing sentence similarity.

MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity