04760nam a22005895i 4500 978-3-031-27226-4 DE-He213 20251030111406.0 cr nn 008mamaa 240130s2024 sz | s |||| 0|eng d 9783031272264 978-3-031-27226-4 10.1007/978-3-031-27226-4 doi QA76.9.N38 UYQL bicssc COM073000 bisacsh UYQL thema Haralambous, Yannis. author. aut http://id.loc.gov/vocabulary/relators/aut 70117 A Course in Natural Language Processing [electronic resource] / by Yannis Haralambous. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVII, 534 p. 102 illus., 74 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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 (Computer science). 65131 Computational linguistics. Expert systems (Computer science). 67822 Artificial intelligence Data processing. Machine learning. 55059 Artificial intelligence. 58441 Natural Language Processing (NLP). 66164 Computational Linguistics. Knowledge Based Systems. 67823 Data Science. Machine Learning. 55059 Symbolic AI. 70118 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031272257 Printed edition: 9783031272271 Printed edition: 9783031272288 https://doi.org/10.1007/978-3-031-27226-4 ZDB-2-SCS ZDB-2-SXCS 57362 57362