Browsing by Author "Keshava, V."

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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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    Constructing an enriched domain taxonomy for Hindi using word embeddings
    (2018) Keshava, V.; Avvara, P.; Sowmya, Kamath S.; Geetha, V.
    Domain-specific taxonomies constitute a valuable resource as they offer extensive support in information retrieval related activities like browsing, searching, recommendations and personalization. Such taxonomies can bridge the gap between the lack of domain-specific querying knowledge in potential users and the actual content. In case of multilingual content, taxonomies can play a pivotal role in boosting search performance for content across language barriers. In this paper, a domain-agnostic framework for building an evolving, domain-specific taxonomy for the Hindi, given a set of well-organized data points is proposed. The approach is intended for designing a hierarchical taxonomy enriched with synonyms and other morphological variants using WordNet and Word2vec models respectively. The hierarchical structure acts as a base which binds the taxonomy to a given domain. Such enrichment can improve taxonomy coverage within the given domain. The focus is also on building a taxonomy that can self-evolve over time, with high precision and recall, with minimal manual effort. � 2017 IEEE.
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    Constructing an enriched domain taxonomy for Hindi using word embeddings
    (Institute of Electrical and Electronics Engineers Inc., 2017) Keshava, V.; Pravalika, P.; Kamath S․, S.S.; Geetha, V.
    Domain-specific taxonomies constitute a valuable resource as they offer extensive support in information retrieval related activities like browsing, searching, recommendations and personalization. Such taxonomies can bridge the gap between the lack of domain-specific querying knowledge in potential users and the actual content. In case of multilingual content, taxonomies can play a pivotal role in boosting search performance for content across language barriers. In this paper, a domain-agnostic framework for building an evolving, domain-specific taxonomy for the Hindi, given a set of well-organized data points is proposed. The approach is intended for designing a hierarchical taxonomy enriched with synonyms and other morphological variants using WordNet and Word2vec models respectively. The hierarchical structure acts as a base which binds the taxonomy to a given domain. Such enrichment can improve taxonomy coverage within the given domain. The focus is also on building a taxonomy that can self-evolve over time, with high precision and recall, with minimal manual effort. © 2017 IEEE.
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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.