@article{(Open Science Index):https://publications.waset.org/pdf/10010427,
	  title     = {Measuring Text-Based Semantics Relatedness Using WordNet},
	  author    = {Madiha Khan and  Sidrah Ramzan and  Seemab Khan and  Shahzad Hassan and  Kamran Saeed},
	  country	= {},
	  institution	= {},
	  abstract     = {Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information.
},
	    journal   = {International Journal of Cognitive and Language Sciences},
	  volume    = {13},
	  number    = {5},
	  year      = {2019},
	  pages     = {312 - 315},
	  ee        = {https://publications.waset.org/pdf/10010427},
	  url   	= {https://publications.waset.org/vol/149},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 149, 2019},
	}