Anoop Kunchukuttan, Pushpak Bhattacharyya
Machine Translation and Transliteration involving Related, Low-resource Languages
Anoop Kunchukuttan, Pushpak Bhattacharyya
Machine Translation and Transliteration involving Related, Low-resource Languages
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This book provides a fresh perspective by focussing on very important class of related languages. It will be relevant to graduate and advanced undergraduate students as well as professionals concerned with Machine Translation, Translation Studies, Natural Language Processing and Multilingual Computing.
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This book provides a fresh perspective by focussing on very important class of related languages. It will be relevant to graduate and advanced undergraduate students as well as professionals concerned with Machine Translation, Translation Studies, Natural Language Processing and Multilingual Computing.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 200
- Erscheinungstermin: 13. August 2021
- Englisch
- Abmessung: 234mm x 156mm x 14mm
- Gewicht: 485g
- ISBN-13: 9780367561994
- ISBN-10: 0367561999
- Artikelnr.: 62227020
- Verlag: Taylor & Francis
- Seitenzahl: 200
- Erscheinungstermin: 13. August 2021
- Englisch
- Abmessung: 234mm x 156mm x 14mm
- Gewicht: 485g
- ISBN-13: 9780367561994
- ISBN-10: 0367561999
- Artikelnr.: 62227020
Dr. Anoop Kunchukuttan is a Senior Applied Researcher in the machine translation team at Microsoft India, Hyderabad. He received his Ph.D from the Indian Institute of Technology Bombay. He is broadly interested in natural language processing and machine learning. His research interests include multilingual learning, language relatedness, machine translation, machine transliteration and distributional semantics. He has also explored problems in information extraction, automated grammar correction, multiword expressions and crowdsourcing for NLP. These works have been published in top-tier Natural Language Processing (NLP) conferences and journals. He is passionate about building software and resources for NLP in Indian languages. He actively develops and maintains the Indic NLP Library and the Indic NLP Catalog, and has contributed to the development of resources like the AI4Bharat Indic NLP Suite and the IIT Bombay parallel corpus. He is a co-organizer of the Workshop on Asian Translation and a co-founder of the AI4Bharat NLP Initiative. Dr. Pushpak Bhattacharyya is Professor of Computer Science and Engineering Department IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP. His textbook 'Machine Translation' sheds light on all paradigms of machine translation with abundant examples from Indian Languages. Two recent monographs co-authored by him called 'Investigations in Computational Sarcasm' and 'Cognitively Inspired Natural Language Processing- An Investigation Based on Eye Tracking' describe cutting edge research in NLP and ML. Prof. Bhattacharyya is Fellow of Indian National Academy of Engineering (FNAE) and Abdul Kalam National Fellow. For sustained contribution to technology he received the Manthan Award of the Ministry of IT, P.K. Patwardhan Award of IIT Bombay and VNMM Award of IIT Roorkey. He is also a Distinguished Alumnus of IIT Kharagpur and past President of Association of Computational Linguistics.
Preface. Introduction. Past Work on MT for Related Languages. I Machine
Translation. Utilizing Lexical Similarity by using Subword Translation
Units. Improving Subword-level. Translation Quality. Subword-level
Pivot-based SMT. A Case Study on Indic Language Translation. II Machine
Transliteration. Utilizing Orthographic Similarity for Unsupervised
Transliteration. Multilingual Neural Transliteration. Conclusion and Future
Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software
and Data Resources. C Conferences/Workshops for Translation between Related
Languages. Bibliography.
Translation. Utilizing Lexical Similarity by using Subword Translation
Units. Improving Subword-level. Translation Quality. Subword-level
Pivot-based SMT. A Case Study on Indic Language Translation. II Machine
Transliteration. Utilizing Orthographic Similarity for Unsupervised
Transliteration. Multilingual Neural Transliteration. Conclusion and Future
Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software
and Data Resources. C Conferences/Workshops for Translation between Related
Languages. Bibliography.
Preface. Introduction. Past Work on MT for Related Languages. I Machine
Translation. Utilizing Lexical Similarity by using Subword Translation
Units. Improving Subword-level. Translation Quality. Subword-level
Pivot-based SMT. A Case Study on Indic Language Translation. II Machine
Transliteration. Utilizing Orthographic Similarity for Unsupervised
Transliteration. Multilingual Neural Transliteration. Conclusion and Future
Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software
and Data Resources. C Conferences/Workshops for Translation between Related
Languages. Bibliography.
Translation. Utilizing Lexical Similarity by using Subword Translation
Units. Improving Subword-level. Translation Quality. Subword-level
Pivot-based SMT. A Case Study on Indic Language Translation. II Machine
Transliteration. Utilizing Orthographic Similarity for Unsupervised
Transliteration. Multilingual Neural Transliteration. Conclusion and Future
Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software
and Data Resources. C Conferences/Workshops for Translation between Related
Languages. Bibliography.