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024 7 _a10.1007/978-3-031-27226-4
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100 1 _aHaralambous, Yannis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_997166
245 1 2 _aA Course in Natural Language Processing
_h[electronic resource] /
_cby Yannis Haralambous.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVII, 534 p. 102 illus., 74 illus. in color.
_bonline resource.
336 _atext
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337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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505 0 _aPreface -- 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.
520 _aNatural 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.
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 0 _aExpert systems (Computer science).
_93392
650 0 _aArtificial intelligence
_xData processing.
_921787
650 0 _aMachine learning.
_91831
650 0 _aArtificial intelligence.
_93407
650 1 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
650 2 4 _aKnowledge Based Systems.
_979172
650 2 4 _aData Science.
_934092
650 2 4 _aMachine Learning.
_91831
650 2 4 _aSymbolic AI.
_997169
710 2 _aSpringerLink (Online service)
_997172
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031272257
776 0 8 _iPrinted edition:
_z9783031272271
776 0 8 _iPrinted edition:
_z9783031272288
856 4 0 _uhttps://doi.org/10.1007/978-3-031-27226-4
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