Quantum Neural Network Based Machine Translator for Hindi to English
Joint Authors
Narayan, Ravi
Singh, V. P.
Chakraverty, Snehashish
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus.
The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human.
The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English.
To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation.
The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.
American Psychological Association (APA)
Narayan, Ravi& Singh, V. P.& Chakraverty, Snehashish. 2014. Quantum Neural Network Based Machine Translator for Hindi to English. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1049792
Modern Language Association (MLA)
Narayan, Ravi…[et al.]. Quantum Neural Network Based Machine Translator for Hindi to English. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1049792
American Medical Association (AMA)
Narayan, Ravi& Singh, V. P.& Chakraverty, Snehashish. Quantum Neural Network Based Machine Translator for Hindi to English. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1049792
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049792