03933nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153072001700168072002300185072001600208100008000224245012100304250001800425264007200443300004500515336002600560337002600586338003600612347002400648505114700672520102101819650005202840650002902892650003202921650002202953650004002975650004603015650003903061650002903100650003203129650002203161650001203183650001803195710003403213773002603247776003603273776003603309776003603345856004603381978-981-99-1999-4DE-He21320251030110832.0cr nn 008mamaa231114s2024    si |    s    |||| 0|eng d  a97898199199947 a10.1007/978-981-99-1999-42doi 4aQA76.9.N38 7aUYQL2bicssc 7aCOM0730002bisacsh 7aUYQL2thema1 aLee, Raymond S. T.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aNatural Language Processingh[electronic resource] :bA Textbook with Python Implementation /cby Raymond S. T. Lee.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXXXII, 437 p. 1 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aPart I – Concepts and Technology -- Chapter 1. Introduction to Natural Language Processing -- Chapter 2. N-gram Language Model -- Chapter 3. Part-of-Speech Tagging -- Chapter 4. Syntax and Parsing -- Chapter 5. Meaning Representation -- Chapter 6. Semantic Analysis -- Chapter 7. Pragmatic Analysis and Discourse -- Chapter 8. Transfer Learning and Transformer Technology -- Chapter 9. Major Natural Language Processing Applications -- Part II –Natural Language Processing Workshops with Python Implementation in 14 Hours -- Chapter 10. Workshop#1 – Basics of Natural Language Toolkit (Hour 1-2) -- Chapter 11. Workshop#2 – N-grams Modeling with Natural Language Toolkit (Hour 3-4) -- Chapter 12. Workshop#3 – Part-of-Speech Tagging using Natural Language Toolkit (Hour 5-6) -- Chapter 13. Workshop#4 – Semantic Analysis and Word Vectors using spaCy (Hour 7-8) -- Chapter 14. Workshop#5 – Sentiment Analysis and Text Classification (Hour 9-10) -- Chapter 15. Workshop#6 – Transformers with spaCy and TensorFlow (Hour11-12) -- Chapter 16. Workshop#7 – Building Chatbot with TensorFlow and Transformer Technology (Hour 13-14).  aThis textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops. 0aNatural language processing (Computer science). 0aArtificial intelligence. 0aComputational intelligence. 0aMachine learning. 0aPython (Computer program language). 0aArtificial intelligencexData processing.14aNatural Language Processing (NLP).24aArtificial Intelligence.24aComputational Intelligence.24aMachine Learning.24aPython.24aData Science.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981991998708iPrinted edition:z978981992000608iPrinted edition:z978981992001340uhttps://doi.org/10.1007/978-981-99-1999-4