Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2630
Title: MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity
Authors: Kasthurirathna, D
Jeyaraj, M. N
Keywords: multi-layer network
network science
semantic similarity
Issue Date: Nov-2021
Citation: Jeyaraj, Manuela & Kasthurirathna, Dharshana. (2021). MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity.
Series/Report no.: International Journal of Engineering Trends and Technology;Volume 69 Issue 7, 181-189
Abstract: 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 questionanswering, 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
URI: http://rda.sliit.lk/handle/123456789/2630
ISSN: 2231 – 5381
Appears in Collections:Department of Computer Science and Software Engineering-Scopes

Files in This Item:
File Description SizeFormat 
MNet-Sim_A_Multi-layered_Semantic_Similarity_Netwo.pdf817.69 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.