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

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

9 Nov 2021IJETT 2021 8arXiv:2111.05412archive 2025-07-28

Manuela Nayantara Jeyaraj, Dharshana Kasthurirathna

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.

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Tasks

Chatbot Document Classification Question Answering Semantic Similarity Semantic Textual Similarity Sentence Sentence Similarity Sentiment Analysis

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semantic Textual Similarity	STS Benchmark	Mnet-Sim	Pearson Correlation	0.927	#3 of 66	Archive leaderboard	report
Semantic Textual Similarity	STS Benchmark	Mnet-Sim	Spearman Correlation	0.931	#3 of 66	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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