Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications

Erscheinungsjahr 2018
ISBN: 978-0-444-64042-0
Verlag: Elsevier Science & Technology

Buch, Englisch, 537 Seiten, Format (B × H): 236 mm x 159 mm, Gewicht: 996 g

Reihe: Handbook of Statistics

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.

Zielgruppe

<p>This monograph is intended to fill the dire need for a scholarly compendium of recent research, transformational and non-traditional applications of natural language understanding. It is intended to serve as an authoritative reference and handbook for industry practitioners, educators, and students alike. The intended audience for the monograph are industry practitioners, researchers, educators, graduate and undergraduate students. </p> <p>The monograph is unique and one of its kind. It unifies linguistics theory, statistical methods, machine learning algorithms, and high-performance computing, which are essential to gain insights into current approaches to natural language processing and understanding. Researchers will benefit from a cohesive and integrated body of knowledge drawn from the underlying disciplines. It will provide them an accessible and convenient resource to quickly learn the state-of-the-art. Many universities are introducing courses in natural language understanding in the backdrop of the immense popularity of IBM Watson, a question-answering system that won Jeopardy! game championship in 2011. This is in contrast with only select research-intensive universities which offered courses in this area until recently. The monograph is suitable for teaching classes at both graduate and undergraduate levels. </p> <p>Several open-source datasets, libraries, application frameworks, and workflow systems are discussed. These resources are valuable for readers who want to engage in experimentation for deepening their understanding.</p>

Autoren/Hrsg.

Weitere Mitwirkende

Gudivada, Venkat N.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung

Inhaltsverzeichnis

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

Über Autor(innen)

Gudivada, Venkat N.
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.