Browsing by Author "Dinesh, A.C."

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    A morphological approach for measuring pair-wise semantic similarity of sanskrit sentences
    (Springer Verlag service@springer.de, 2017) Keshava, V.; Sanapala, M.; Dinesh, A.C.; Kamath S․, S.S.
    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.
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    A morphological approach for measuring pair-wise semantic similarity of sanskrit sentences
    (2017) Keshava, V.; Sanapala, M.; Dinesh, A.C.; Shevgoor, S.K.
    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.