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Multimedia Systems
Published in: Multimedia Systems 6/2022

11-10-2020 | Special Issue Paper

Sanskrit to universal networking language EnConverter system based on deep learning and context-free grammar

Authors: Sitender, Seema Bawa

Published in: Multimedia Systems | Issue 6/2022

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Abstract

Machine Translation is a mechanism of transforming text from one language to another with the help of computer technology. Earlier in 2018, a machine translation system had been developed by the authors that translate Sanskrit text to Universal Networking Language expressions and was named as SANSUNL. The work presented in this paper is an extension of SANSUNL system by enhancing POS tagging, Sanskrit language processing and parsing. A Sanskrit stemmer having 23 prefixes and 774 suffixes with grammar rules are used for stemming the Sanskrit sentence in the proposed system. Bidirectional long short-term memory (Bi-LSTM) and stacked LSTM deep neural network models have been used for part of speech tagging of the input Sanskrit text. A tagged dataset of around 400 k entries for Sanskrit have been used for training and testing the neural network models. Proposed Sanskrit context-free grammar has been used with CYK parser to perform the parsing of the input sentence. Size of the Sanskrit-Universal Word dictionary has been increased from 15000 to 25000 entries. Approximately 1500 UNL generation rules have been used to resolve the 46 UNL relations. Four datasets UC-A1, UC-A2, Spanish server gold standard dataset, and 500 Sanskrit sentences taken from the general domain have been used for validating the system. The proposed system is evaluated on BLEU and Fluency score metrics and has reported an efficiency of 95.375%.
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Metadata
Title
Sanskrit to universal networking language EnConverter system based on deep learning and context-free grammar
Authors
Sitender
Seema Bawa
Publication date
11-10-2020
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 6/2022
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-020-00692-3

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