Intelligent System for Automatic Transfer Grammar Creation Using Parallel Corpus

Singh, Shashi Pal; Kumar, Ajai; Darbari, Hemant; Singh, Lenali; Joshi, Nisheeth; Gupta, Priya; Singh, Sneha

doi:10.1007/978-3-319-63645-0_59

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Asopa et al [42] developed a shallow parser for Hindi using conditional random fields. Gupta et al [43] showed the use of machine learning approached in developing NLP applications. Gupta et al [44] used fuzzy operations in analyzing sentiments of tweets on several topics.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Part of Speech Tagger Using Deep Learning

Singh*¹,

Verma²,

Seal³

et al. 2019

IJEAT

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Part of speech tagging is the initial step in development of NLP (natural language processing) application. POS Tagging is sequence labelling task in which we assign Part-of-speech to every word (Wi) which is sequence in sentence and tag (Ti) to corresponding word as label such as (Wi/Ti…. Wn/Tn). In this research project part of speech tagging is perform on Hindi. Hindi is the fourth most popular language and spoken by approximately 4billion people across the globe. Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. In this paper we have shown the development of POS tagging using neural approach.

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Section: Literature Reviewmentioning

confidence: 99%

Development of Part of Speech Tagger Using Deep Learning

Singh*¹,

Verma²,

Seal³

et al. 2019

IJEAT

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“…This significantly improved the performance of their MT engine. Joshi et al [33] investigated some approaches to classifying documents and further suggested an approach for effective classification of text documents. Singh et al [34] developed an approach to automatically generate transfer grammar rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of English-Hindi Interactive Machine Translation

Sharma¹,

Seth²,

Syed³

et al. 2019

IJEAT

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Machine Translation systems are still far from being perfect and to improve their performance the concept of Interactive Machine Translation (IMT) was introduced. This paper proposes an IMT system, which uses Statistical Machine Translation and a bilingual corpus on which several algorithms (Word error rate, Position Independent Error Rate, Translation Error Rate, n-grams) are implemented to translate text from English to Indian languages. The proposed system improves both the speed and productivity of the human translators as found through experiments.

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Intelligent System for Automatic Transfer Grammar Creation Using Parallel Corpus

Cited by 2 publications

References 6 publications

Development of Part of Speech Tagger Using Deep Learning

Development of Part of Speech Tagger Using Deep Learning

Development of English-Hindi Interactive Machine Translation

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