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Autor(en): 
  • Yannis Haralambous
  • A Course in Natural Language Processing 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Februar 2025  
    Genre:  EDV / Informatik 
     
    Computational Linguistics / Computerlinguistik und Korpuslinguistik / Controlled Natural Languages / Data Science / Datenbanken / formal languages / Grammars / Graphemics
    ISBN:  9783031272288 
    EAN-Code: 
    9783031272288 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  534 
    Illustration:  XVII, 534 p. 102 illus., 74 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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