自然言語処理:Python実装テキスト<br>Natural Language Processing : A Textbook with Python Implementation

個数:1
  • 電子書籍

自然言語処理:Python実装テキスト
Natural Language Processing : A Textbook with Python Implementation

  • 著者名:Lee, Raymond S. T.
  • 価格 ¥11,190 (本体¥10,173)
  • Springer(2023/11/14発売)
  • ポイント 101pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9789819919987
  • eISBN:9789819919994

ファイル: /

Description

This 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.

Table of Contents

Part 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).

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