Please use this identifier to cite or link to this item:
Title: A morphological approach for measuring pair-wise semantic similarity of sanskrit sentences
Authors: Keshava, V.
Sanapala, M.
Dinesh, A.C.
Shevgoor, S.K.
Issue Date: 2017
Citation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, Vol.10260 LNCS, , pp.162-169
Abstract: Capturing 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.
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
8 A Morphological Approach.pdf315.36 kBAdobe PDFThumbnail

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