  Similarity is a comparative-subjective measure that varies with the domain
within which it is considered. In several NLP applications such as document
classification, pattern recognition, chatbot question-answering, sentiment
analysis, etc., identifying an accurate similarity score for sentence pairs has
become a crucial area of research. In the existing models that assess
similarity, the limitation of effectively computing this similarity based on
contextual comparisons, the localization due to the centering theory, and the
lack of non-semantic textual comparisons have proven to be drawbacks. Hence,
this paper presents a multi-layered semantic similarity network model built
upon multiple similarity measures that render an overall sentence similarity
score based on the principles of Network Science, neighboring weighted
relational edges, and a proposed extended node similarity computation formula.
The proposed multi-layered network model was evaluated and tested against
established state-of-the-art models and is shown to have demonstrated better
performance scores in assessing sentence similarity.
