A Course in Natural Language Processing

Beschreibung

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. 
Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). 
Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study. 
With a PhD in Algebraic Topology (Lille, 1990), Yannis Haralambous is a TeX aficionado and Full Professor at IMT Atlantique in Brest, France. His research interests cover Text Mining, Controlled Natural Languages, Knowledge Representation, and Grapholinguistics, topics in which he has published over 120 research or scientific popularization papers and a book on Fonts and Encodings (O’Reilly, 2007). He is in charge of IMT Atlantique’s “Data Science” Master track program, where he has been teaching the course that inspired this book.
Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. 
Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). 
Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book. This material can serve for classes given to undergraduate and graduate students, or for researchers, instructors, and professionals in computer science or linguistics who wish to acquire or improve their knowledge in the field. The book is suitable and warmly recommended for self-study.
Offers a first, self-contained course to NLP based on a class taught by the author for over ten years Includes supplementary teaching material such as labs, exams or answers to general questions Presents a three-fold approach to learning a discipline at the crossroad of Linguistics, Mathematics and AI Request lecturer material: sn.pub/lecturer-material

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Yannis Haralambous

Themen in »A Course in Natural Language Processing«

Controlled Natural Languages Formal Languages Grammars Graphemics Graphetics LaTeX Linguistics Natural Language Processing Neural Networks Phonetics Semantics Syntax Text Mining

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“This book adopts a reader-focused approach, making it accessible to a wide audience. Each chapter ends with a section titled “Further readings”, which offers suggestions for those interested in exploring specific topics in greater depth. … This book will hold a significant place in the current NLP literature. ... Its interdisciplinary approach, well-structured pedagogical framework, and emphasis on both theoretical and practical aspects make it a valuable addition to the library of any serious NLP researcher or practitioner.” (Korhan Günel, zbMATH 1562.68005, 2025)

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