000			03194nam a22003257a 4500
003			OSt
005			20260925090722.0
008			260925b |||||||| |||| 00| 0 eng d
020			_a9783031272288 [paperback]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
100			_aHaralambous, Yannis, _eauthor.
245			_aA course in natural language processing / _cYannis Haralambous.
260			_aSwitzerland : _bSpringer, _cc2024.
300			_axvii, 534 pages : _billustrations (black & white) ; _c23.4 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes index..
505			_aContents: 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.
520			_a"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." —Provided by the publisher
521			_aAdult
541			_aPurchased _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aNatural language processing (Computer science).
650			_aComputational linguistics.
650			_aMachine learning.
942			_2ddc _cSR
998			_cGian [new] _d09/25/2026
999			_c15697 _d15697