Book ChapterDOI
Peer Analysis of “Sanguj” with Other Sanskrit Morphological Analyzers
Jatinderkumar R. Saini,Jatinderkumar R. Saini,Jaideepsinh K. Raulji,Jaideepsinh K. Raulji +3 more
- pp 65-73
TLDR
Here, 328 Sanskrit words are tested through four morphological analyzers namely—Samsaadhanii, morphological Analyzers by JNU and TDIL, both of which are available online and locally developed and installed Sanguj morphological analyzezer.Abstract:
In linguistics, morphology is a study regarding word, word formation, its analysis, and generation. A morphological analyzer is a tool to understand grammatical characteristics and constituent’s part-of-speech information. A morphological analyzer is a useful tool in many NLP implementations such as syntactic parser, spell checker, information retrieval, and machine translation. Here, 328 Sanskrit words are tested through four morphological analyzers namely—Samsaadhanii, morphological analyzers by JNU and TDIL, both of which are available online and locally developed and installed Sanguj morphological analyzer. There is a negligible divergence in the reflected results.read more
Citations
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Journal ArticleDOI
Measuring the Similarity between the Sanskrit Documents using the Context of the Corpus
TL;DR: The proposed approach processes the oldest, untouched, one of the morphologically critical languages, Sanskrit and builds a document term matrix for Sanskrit (DTMS) and Document synset matrix Sanskrit (DSMS) to solve the problem of polysemy.
Journal ArticleDOI
A Novel Framework for Sanskrit-Gujarati Symbolic Machine Translation System
TL;DR: In this paper , a machine translation framework using a grammatical transfer approach to translate the written Sanskrit language to Gujarati has been proposed and realized, which uses a tokenization, lemmatization, morphological analysis, bilingual synonym-based dictionary, language synthesis, and transliteration.
References
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A Novel PSO Based Back Propagation Learning-MLP (PSO-BP-MLP) for Classification
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SanskritTagger: A Stochastic Lexical and POS Tagger for Sanskrit
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Punjabi Poetry Classification: The Test of 10 Machine Learning Algorithms
TL;DR: Results for Punjabi poetry classification revealed that 4 machine learning algorithms namely, Hyperpipes (HP), K- nearest neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) with an accuracy of 50.63 %, 52.75 % and 58.79 % respectively, outperformed all other machinelearning algorithms under the test.