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| 001 | 978-3-031-27226-4 | ||
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| 005 | 20251030111406.0 | ||
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_a10.1007/978-3-031-27226-4 _2doi |
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| 100 | 1 |
_aHaralambous, Yannis. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _970117 |
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| 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. |
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| 300 |
_aXVII, 534 p. 102 illus., 74 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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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). _965131 |
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| 650 | 0 | _aComputational linguistics. | |
| 650 | 0 |
_aExpert systems (Computer science). _967822 |
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| 650 | 0 |
_aArtificial intelligence _xData processing. |
|
| 650 | 0 |
_aMachine learning. _955059 |
|
| 650 | 0 |
_aArtificial intelligence. _958441 |
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| 650 | 1 | 4 |
_aNatural Language Processing (NLP). _966164 |
| 650 | 2 | 4 | _aComputational Linguistics. |
| 650 | 2 | 4 |
_aKnowledge Based Systems. _967823 |
| 650 | 2 | 4 | _aData Science. |
| 650 | 2 | 4 |
_aMachine Learning. _955059 |
| 650 | 2 | 4 |
_aSymbolic AI. _970118 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 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 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 999 |
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