Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more.
The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important.
Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more.
The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important.
1. Linguistics: Core Concepts and Principles
2. Grammars
3. Open-Source Libraries, Application Frameworks, Workflow Systems,
and Other Resources
4. Mathematical Essentials
5. Probability
6. Inference and Prediction Methods
7. Random Processes
8. Bayesian Methods
9. Machine Learning
10. Artificial Neural Networks for Natural Language Processing
11. Information Retrieval
12. Language Core Tasks 1
13. Language Core Tasks 2
14. Language Understanding Applications 1
15. Language Understanding Applications 2
16. Deep Learning for Natural Language Processing
17. Text Mining for Modeling Cyberattacks
18. World Languages and Crosslinguistics
19. Linguistic Elegance of the Languages of South India
20. Current Trends and Open Problems
C. R. Rao is a world famous statistician who earned a place in the
history of statistics as one of those “who developed statistics
from its adhoc origins into a firmly grounded mathematical
science.
He was employed at the Indian Statistical Institute (ISI) in 1943
as a research scholar after obtaining an MA degree in mathematics
with a first class and first rank from Andhra University in1941 and
MA degree in statistics from Calcutta University in 1943 with a
first class, first rank, gold medal and record marks which remain
unbroken during the last 73 years.
“At the age of 28 he was made a full professor at ISI in
recognition of his creativity. While at ISI, Rao went to Cambridge
University (CU) in 1946 on an invitation to work on an
anthropometric project using the methodology developed at ISI. Rao
worked in the museum of archeology and anthropology in Duckworth
laboratory of CU during 1946-1948 as a paid visiting scholar. The
results were reported in the book “Ancient Inhabitants of Jebel
Moya published by the Cambridge Press under the joint authorship
of Rao and two anthropologists. On the basis of work done at CU
during the two year period, 1946-1948, Rao earned a Ph.D. degree
and a few years later Sc.D. degree of CU and the rare honor of life
fellowship of Kings College, Cambridge.
He retired from ISI in 1980 at the mandatory age of 60 after
working for 40 years during which period he developed ISI as an
international center for statistical education and research. He
also took an active part in establishing state statistical bureaus
to collect local statistics and transmitting them to Central
Statistical Organization in New Delhi. Rao played a pivitol role in
launching undergraduate and postgraduate courses at ISI. He is the
author of 475 research publications and several breakthrough papers
contributing to statistical theory and methodology for applications
to problems in all areas of human endeavor. There are a number of
classical statistical terms named after him, the most popular of
which are Cramer-Rao inequality, Rao-Blackwellization, Rao’s
Orthogonal arrays used in quality control, Rao’s score test, Rao’s
Quadratic Entropy used in ecological work, Rao’s metric and
distance which are incorporated in most statistical books.
He is the author of 10 books, of which two important books are,
Linear Statistical Inference which is translated into German,
Russian, Czec, Polish and Japanese languages,and Statistics and
Truth which is translated into, French, German, Japanese, Mainland
Chinese, Taiwan Chinese, Turkish and Korean languages.
He directed the research work of 50 students for the Ph.D. degrees
who in turn produced 500 Ph.D.’s. Rao received 38 hon. Doctorate
degree from universities in 19 countries spanning 6 continents. He
received the highest awards in statistics in USA,UK and India:
National Medal of Science awarded by the president of USA, Indian
National Medal of Science awarded by the Prime Minister of India
and the Guy Medal in Gold awarded by the Royal Statistical Society,
UK. Rao was a recipient of the first batch of Bhatnagar awards in
1959 for mathematical sciences and and numerous medals in India and
abroad from Science Academies. He is a Fellow of Royal Society
(FRS),UK, and member of National Academy of Sciences, USA,
Lithuania and Europe. In his honor a research Institute named as
CRRAO ADVANCED INSTITUTE OF MATHEMATICS, STATISTICS AND COMPUTER
SCIENCE was established in the campus of Hyderabad University.
Venkat N. Gudivada is a professor and chair of the Computer Science
Department at East Carolina University. Prior to this, he was a
professor and founding chair of the Weisberg Division of Computer
Science at Marshall University. His industry tenure spans over six
years as a vice president for Wall Street companies in the New York
City area including Merrill Lynch (now Bank of America Merrill
Lynch) and Financial Technologies International (now GoldenSource).
Previous academic tenure includes work at the University of
Michigan, University of Missouri, and Ohio University.He has
published over 90 peer-reviewed technical articles and rendered
professional service in various roles including conference program
chair, keynote speaker, program committee member, and guest editor
of IEEE journals. Gudivada's research sponsors include National
Science Foundation (NSF), National Aeronautics and Space
Administration (NASA), U.S. Department of Energy, U.S. Department
of Navy, U.S. Army Research Office, MU Foundation, and WV Division
of Science and Research. His current research interests encompass
Big Data Management, High Performance Computing, Information
Retrieval, Image and Natural Language Processing, and Personalized
Learning. Gudivada received a PhD degree in Computer Science from
the University of Louisiana at Lafayette.
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