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DC Field | Value | Language |
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dc.contributor.author | Keshava, V. | - |
dc.contributor.author | Sanapala, M. | - |
dc.contributor.author | Dinesh, A.C. | - |
dc.contributor.author | Shevgoor, S.K. | - |
dc.date.accessioned | 2020-03-30T09:58:25Z | - |
dc.date.available | 2020-03-30T09:58:25Z | - |
dc.date.issued | 2017 | - |
dc.identifier.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 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/7018 | - |
dc.description.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. | en_US |
dc.title | A morphological approach for measuring pair-wise semantic similarity of sanskrit sentences | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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8 A Morphological Approach.pdf | 315.36 kB | Adobe PDF | View/Open |
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