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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.
List of contents
Preface.- 1. Introduction.- Part I. Linguistics.- 2. Phonetics/Phonology.- 3. Graphetics/Graphemics.- 4. Morphemes, Words, Terms.- 5. Syntax.- 6. Semantics (and Pragmatics).- 7. Controlled Natural Languages.- Part II. Mathematical Tools.- 8. Graphs.- 9. Formal Languages.- 10. Logic.- 11.- Ontologies and Conceptual Graphs.- Part III. Data Formats.- 12. Unicode.- 13. XML, TEI, CDL.- Part IV. Statistical Methods.- 14. Counting Words.- 15. Going Neural.- 16. Hints and Expected Results for Exercises.- Acronyms.- Index.
About the author
Born in Athens, Greece, Yannis Haralambous studied Mathematics in Lille, France, where he obtained a Ph.D. in Algebraic Topology in 1990. Having meanwhile become a TeX aficionado, he then specialized in Digital Typography and founded the typesetting company Atelier Fluxus Virus, which is specialized in scientific and scholarly documents. In 2001, he became a Full Professor at the Computer Science Department of IMT Atlantique in Brest, France, and his research activities migrated to the disciplines of Text Mining, Controlled Natural Languages, Knowledge Representation, and Grapholinguistics. He has published more than 120 research or scientific popularization papers and a book on Fonts and Encodings (O'Reilly, 2004), has supervised 10 PhDs, teaches courses on NLP, Graph Theory and Logic, and is the organizer of the biennial conference “Grapholinguistics in the 21st Century”.
Summary
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.
