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dc.contributor.authorKeshava, V.-
dc.contributor.authorSanapala, M.-
dc.contributor.authorDinesh, A.C.-
dc.contributor.authorShevgoor, S.K.-
dc.date.accessioned2020-03-30T09:58:25Z-
dc.date.available2020-03-30T09:58:25Z-
dc.date.issued2017-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, Vol.10260 LNCS, , pp.162-169en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7018-
dc.description.abstractCapturing explicit and implicit similarity between texts in natural language is a critical task in Computational Linguistics applications. Similarity can be multi-level (word, sentence, paragraph or document level), each of which can affect the similarity computation differently. Most existing techniques are ill-suited for classical languages like Sanskrit as it is significantly richer in morphology than English. In this paper, we present a morphological analysis based approach for computing semantic similarity between short Sanskrit texts. Our technique considers the constituent words� semantic properties and their role in individual sentences within the text, to compute similarity. As all words do not contribute equally to the semantics of a sentence, an adaptive scoring algorithm is used for ranking, which performed very well for Sanskrit sentence pairs of varied complexities. � Springer International Publishing AG 2017.en_US
dc.titleA morphological approach for measuring pair-wise semantic similarity of sanskrit sentencesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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