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Machine Translation and Transliteration involving Related,
Low-resource Languages discusses an important aspect of natural
language processing that has received lesser attention: translation
and transliteration involving related languages in a low-resource
setting. This is a very relevant real-world scenario for people
living in neighbouring states/provinces/countries who speak similar
languages and need to communicate with each other, but training
data to build supporting MT systems is limited. The book discusses
different characteristics of related languages with rich examples
and draws connections between two problems: translation for related
languages and transliteration. It shows how linguistic similarities
can be utilized to learn MT systems for related languages with
limited data. It comprehensively discusses the use of subword-level
models and multilinguality to utilize these linguistic
similarities. The second part of the book explores methods for
machine transliteration involving related languages based on
multilingual and unsupervised approaches. Through extensive
experiments over a wide variety of languages, the efficacy of these
methods is established. Features Novel methods for machine
translation and transliteration between related languages,
supported with experiments on a wide variety of languages. An
overview of past literature on machine translation for related
languages. A case study about machine translation for related
languages between 10 major languages from India, which is one of
the most linguistically diverse country in the world. The book
presents important concepts and methods for machine translation
involving related languages. In general, it serves as a good
reference to NLP for related languages. It is intended for
students, researchers and professionals interested in Machine
Translation, Translation Studies, Multilingual Computing Machine
and Natural Language Processing. It can be used as reference
reading for courses in NLP and machine translation. Anoop
Kunchukuttan is a Senior Applied Researcher at Microsoft India. His
research spans various areas on multilingual and low-resource NLP.
Pushpak Bhattacharyya is a Professor at the Department of Computer
Science, 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.
This book shows ways of augmenting the capabilities of Natural
Language Processing (NLP) systems by means of cognitive-mode
language processing. The authors employ eye-tracking technology to
record and analyze shallow cognitive information in the form of
gaze patterns of readers/annotators who perform language processing
tasks. The insights gained from such measures are subsequently
translated into systems that help us (1) assess the actual
cognitive load in text annotation, with resulting increase in human
text-annotation efficiency, and (2) extract cognitive features
that, when added to traditional features, can improve the accuracy
of text classifiers. In sum, the authors' work successfully
demonstrates that cognitive information gleaned from human
eye-movement data can benefit modern NLP. Currently available
Natural Language Processing (NLP) systems are weak AI systems: they
seek to capture the functionality of human language processing,
without worrying about how this processing is realized in human
beings' hardware. In other words, these systems are oblivious to
the actual cognitive processes involved in human language
processing. This ignorance, however, is NOT bliss! The accuracy
figures of all non-toy NLP systems saturate beyond a certain point,
making it abundantly clear that "something different should be
done."
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Smart and Innovative Trends in Next Generation Computing Technologies - Third International Conference, NGCT 2017, Dehradun, India, October 30-31, 2017, Revised Selected Papers, Part I (Paperback, 1st ed. 2018)
Pushpak Bhattacharyya, Hanumat G. Sastry, Venkatadri Marriboyina, Rashmi Sharma
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R3,641
Discovery Miles 36 410
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Ships in 10 - 15 working days
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The two-volume set CCIS 827 and 828 constitutes the thoroughly
refereed proceedings of the Third International Conference on Next
Generation Computing Technologies, NGCT 2017, held in Dehradun,
India, in October 2017. The 135 full papers presented were
carefully reviewed and selected from 948 submissions. There were
organized in topical sections named: Smart and Innovative Trends in
Communication Protocols and Standards; Smart and Innovative Trends
in Computational Intelligence and Data Science; Smart and
Innovative Trends in Image Processing and Machine Vision; Smart
Innovative Trends in Natural Language Processing for Indian
Languages; Smart Innovative Trends in Security and Privacy.
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Smart and Innovative Trends in Next Generation Computing Technologies - Third International Conference, NGCT 2017, Dehradun, India, October 30-31, 2017, Revised Selected Papers, Part II (Paperback, 1st ed. 2018)
Pushpak Bhattacharyya, Hanumat G. Sastry, Venkatadri Marriboyina, Rashmi Sharma
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R3,184
Discovery Miles 31 840
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Ships in 10 - 15 working days
|
The two-volume set CCIS 827 and 828 constitutes the thoroughly
refereed proceedings of the Third International Conference on Next
Generation Computing Technologies, NGCT 2017, held in Dehradun,
India, in October 2017. The 135 full papers presented were
carefully reviewed and selected from 948 submissions. There were
organized in topical sections named: Smart and Innovative Trends in
Communication Protocols and Standards; Smart and Innovative Trends
in Computational Intelligence and Data Science; Smart and
Innovative Trends in Image Processing and Machine Vision; Smart
Innovative Trends in Natural Language Processing for Indian
Languages; Smart Innovative Trends in Security and Privacy.
|
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