Resolving issues in parsing technique in machine translation from hindi language to english language

Mall, Shachi; Jaiswal, Umesh Chandra

doi:10.1109/iccct.2014.7001469

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Similarly, Sinha (2009b), Sinha (2009c) and Dey and Haque (2009) proposed the systems for name, predicate and opinion mining, respectively. Arora and Sinha (2012), Joshi et al (2014), and Mall and Jaiswal (2014) presented their works for MT, and Tyagi et al (2015) proposed solutions for MT and text simplification problems with DC1 documents only. Prasad and Fousiya (2015) recognized named entities, whereas Bharadwaj and Varma (2011) and Kumar and Singh (2017) identified the parallel sentences and mixed code, respectively, using DC1 and DC2 documents.…”

Section: Study I Findings For Applications and Dcsmentioning

confidence: 99%

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

Singh

2020

International Journal of Applied Metaheuristic Computing

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Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

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Section: Study I Findings For Applications and Dcsmentioning

confidence: 99%

Advanced Applications on Bilingual Document Analysis and Processing Systems

Puri

Singh

2020

International Journal of Applied Metaheuristic Computing

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“…Coke-Kasami-Younger (CKY) algorithm is one of them (Harrison, 1978). This algorithm is used for translation from Hindi to English in Mall and Jaiswal (2014).…”

Section: Many Parsing Algorithms Have Been Developed For Syntax Analymentioning

confidence: 99%

Hindi word correction using micro-parsing

Kushwah¹,

Joshi²

2019

IJART

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Hindi has become very popular on the internet in recent times. The Hindi literatures, like stories, poems, etc., are attracting people worldwide. Though lots of work has been done to computerise Hindi, still there are few areas no one has attempted so far, word analysis is one of them. To analyse any language, one is required to possess proper knowledge of symbols and grammar rules of the language. A word is one of the basic meaningful entities and hence correctness of it is of utmost importance in the parlance of natural language processing (NLP). For Hindi word analysis, micro-parsing algorithm has already been developed. This algorithm only detects an illegal Hindi word. In this paper we present an algorithm that is based on micro-parsing and is capable of correcting an illegal Hindi word along with suggestion for possible correct substitution(s). This will definitely encourage and help people write correct Hindi on the internet, and otherwise, too.

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An Efficient Hindi Text Classification Model Using SVM

Puri

Singh

2019

Lecture Notes in Networks and Systems

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Resolving issues in parsing technique in machine translation from hindi language to english language

Cited by 7 publications

References 5 publications

Advanced Applications on Bilingual Document Analysis and Processing Systems

Advanced Applications on Bilingual Document Analysis and Processing Systems

Hindi word correction using micro-parsing

An Efficient Hindi Text Classification Model Using SVM

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