Overview
- Emphasises the need for language resource development and its impact on society
- Covers latest AI based tools and techniques used to preserve indigenous and endangered languages
- Highlights latest AI based technologies such as GPT towards endangered language preservation
Part of the book series: Studies in Computational Intelligence (SCI, volume 1148)
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About this book
This book emphasises the need for language resource development and its impact on society. It covers latest AI based tools and techniques used to preserve indigenous and endangered languages. The book also highlights latest AI based technologies such as Generative Pre-trained Transformer (GPT) towards endangered language preservation. It discusses morphology analysis, translation support and shallow parsing of various tribal languages of India and abroad. This book tries to answer how digital technologies can make language revitalization accessible to future generations.
Keywords
Table of contents (17 chapters)
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Front Matter
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Language Revitalization & Artificial Intelligence
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Front Matter
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Natural Language Process (NLP) for Language Analysis
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Front Matter
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Editors and Affiliations
About the editors
Dr. Sushree Sangita Mohanty currently working as an Assistant Professor in the department of
Anthropology as well as leading the project Mother Tongue based Multilingual Education at Kalinga
Institute of Social Sciences (KISS) which has recently received the UNESCO International Literary
Prize 2022. Her expertise in Multilingual Education facilitates easing the process to build a strong
educational foundation among the indigenous children of KISS. Her research interests are
multidisciplinary in nature which centres around socio-cultural life, multilingualism and livelihood
vulnerability of indigenous & low-income communities of Odisha/India. She has been listed as a
UNESCO Inclusive Policy Lab Expert.
Dr. Satya Ranjan Dash is currently working as an associate professor at KIIT University, India. His
current research includes Epileptic Seizure Detection based on EEG Signal through Spiking neural
network (SNN), Classification of Schizophrenia Patients from EEG and fMRI using SNN and SSN,
fetal heart rate signals classification through extreme learning machine (ELM), Mammogram
Analysis with Local binary pattern (LBP), generative adversarial network (GAN) model, Machine
Learning , Medical Image Processing, Machine Translation, Natural Language Processing and Fuzzy
Mathematical Models.
Dr. Shantipriya Parida currently working as a Senior AI Scientist at Silo AI, Finland. Before joining
Silo AI, Shantipriya worked as a Postdoctoral Researcher at Idiap Research Institute, Switzerland.
He has obtained his Postdoc in Machine Translation from Charles University, Prague, Czech
Republic, Ph.D. in Computational Neuroscience from Utkal University, Odisha, India. Before joining
postdoc at Charles University, he worked as a System Architect at Huawei Technologies India Pvt
Ltd, Bangalore, India. He has 15 years of experience in software development and architecture, as
well as expertise in machine learning, deep learning, and Natural Language Processing. He has 4
years of research experience in leading NLP tasks in EU H2020 and InnoSuisse projects with
publications in top-tier conferences and journals. He is part of the program committee/organizer
for many top-tier NLP conferences and workshops. Recently published an edited book “Natural
Language Processing in Healthcare: A Special Focus on Low Resource Language” in collaboration
with other NLP researchers.
Bibliographic Information
Book Title: Applying AI-Based Tools and Technologies Towards Revitalization of Indigenous and Endangered Languages
Editors: Sushree Sangita Mohanty, Satya Ranjan Dash, Shantipriya Parida
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-981-97-1987-7
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
Hardcover ISBN: 978-981-97-1986-0Published: 24 April 2024
Softcover ISBN: 978-981-97-1989-1Due: 08 May 2025
eBook ISBN: 978-981-97-1987-7Published: 23 April 2024
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 220
Number of Illustrations: 44 b/w illustrations, 25 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Natural Language Processing (NLP)