CSSM: A context-based semantic similarity measure

Titre	CSSM: A context-based semantic similarity measure
Publication Type	Conference Paper
Year of Publication	2020
Authors	Yousfi, A, Yazidi, MHEl, Zellou, A
Conference Name	2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020
Mots-clés	Benchmark datasets, Computer science, Computers, Context-based semantics, Control engineering, Data-sources, Distributed data sources, Electronics engineering, Electronics industry, High-accuracy, Semantic similarity, Semantic similarity approaches, Semantics
Abstract	Semantic similarity is very critical in applications that manipulate data across heterogeneous, autonomous and distributed data sources. It helps connect these data sources. Nevertheless, current semantic similarity approaches were proven to achieve a very moderate accuracy. Furthermore, they are merely applicable when we wish to determine the semantic similarity between words. In this paper, we present CSSM, a Context-based Semantic Similarity Measure. Experimental results on Miller Charles benchmark dataset show that CSSM performs semantic similarity comparisons properly, and obtains high accuracy. © 2020 IEEE.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100120972&doi=10.1109%2fICECOCS50124.2020.9314376&partnerID=40&md5=3191da8d69fa40844c69e41c268c5a17
DOI	10.1109/ICECOCS50124.2020.9314376