Formal Analysis for Natural Language Processing: A Handbook
- 15h 1m
- Zhiwei Feng
- Springer
- 2023
The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is now rapidly evolving, as new methods and toolsets converge with an ever-expanding wealth of available data. This state-of-the-art handbook addresses all aspects of formal analysis for natural language processing. Following a review of the field’s history, it systematically introduces readers to the rule-based model, statistical model, neural network model, and pre-training model in natural language processing.
At a time characterized by the steady and vigorous growth of natural language processing, this handbook provides a highly accessible introduction and much-needed reference guide to both the theory and method of NLP. It can be used for individual study, as the textbook for courses on natural language processing or computational linguistics, or as a supplement to courses on artificial intelligence, and offers a valuable asset for researchers, practitioners, lecturers, graduate and undergraduate students alike.
About the Author
Feng Zhiwei is a computational linguist and senior research fellow at the Institute of Applied Linguistics, Ministry of Education, China. He has a broad and extensive background in linguistics, mathematics and computer science, and has been engaged in interdisciplinary research in linguistics, mathematics and computer science for more than 50 years. One of the first natural language processing and computational linguistics scholars in China, he has published more than 30 books and more than 400 papers in China and abroad. He is the winner of the NLPCC (Natural Language Processing & Chinese Computing) Distinguished Achievement Award of the CCF (China Computer Federation) in 2018.
In this Book
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Past and Present of Natural Language Processing
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Pioneers in the Study of Language Computing
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Formal Models Based on Phrase Structure Grammar
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Formal Models Based on Unification
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Formal Models Based on Dependency and Valence
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Formal Models Based on Lexicalism
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Formal Models of Automatic Semantic Processing
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Formal Models of Automatic Situation and Pragmatic Processing
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Formal Models of Discourse Analysis
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Formal Models of Probabilistic Grammar
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Formal Models of Neural Network and Deep Learning
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Knowledge Graphs
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Concluding Remarks
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Epilogue
