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024 7 _a10.1007/978-981-99-1999-4
_2doi
050 4 _aQA76.9.N38
072 7 _aUYQL
_2bicssc
072 7 _aCOM073000
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072 7 _aUYQL
_2thema
100 1 _aLee, Raymond S. T.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967188
245 1 0 _aNatural Language Processing
_h[electronic resource] :
_bA Textbook with Python Implementation /
_cby Raymond S. T. Lee.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXXXII, 437 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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_2rdacarrier
347 _atext file
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505 0 _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).
520 _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.
650 0 _aNatural language processing (Computer science).
_965131
650 0 _aArtificial intelligence.
_958441
650 0 _aComputational intelligence.
_964052
650 0 _aMachine learning.
_955059
650 0 _aPython (Computer program language).
_964608
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aNatural Language Processing (NLP).
_966164
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aComputational Intelligence.
_964052
650 2 4 _aMachine Learning.
_955059
650 2 4 _aPython.
_947553
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819919987
776 0 8 _iPrinted edition:
_z9789819920006
776 0 8 _iPrinted edition:
_z9789819920013
856 4 0 _uhttps://doi.org/10.1007/978-981-99-1999-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
999 _c56548
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