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SEMANTIC TEXTUAL SIMILARITY USING MACHINE LEARNING ALGORITHMS
V Sowmya, K Kranthi Kiran, Tilak Putta

Published in: International Journal of Current Engineering And Scientific Research ( IJCESR)
Volume- 4, Issue-8, pp.10-15, Aug 2017
DPI :-> 16.10046.IJCESR.2017.V4I8.1015.1872



Abstract
Sentence similarity measures plays a key role in text-related research and applications in areas like as text mining, natural language processing, information extraction, etc. Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two text fragments, though the sentence pair has different words. The text fragments are word phrases, sentences, paragraphs or documents. This paper describes various regression techniques of supervised model used to analyze the impact of syntactic and semantic features in calculating the degree of STS. The empirical evaluation done on SemEval-2017 datasets.

Key-Words / Index Term
Regression Techniques, Semantic, Semantic Textual Similarity, Supervised model, Syntactic

How to cite this article
V Sowmya, K Kranthi Kiran, Tilak Putta , “SEMANTIC TEXTUAL SIMILARITY USING MACHINE LEARNING ALGORITHMS”, International Journal of Current Engineering And Scientific Research ( IJCESR), 4, Issue-8, pp.10-15, Aug 2017. DPI:16.10046.IJCESR.V4.I8.1872