Practical Natural Language Processing with Python
With Case Studies from Industries Using Text Data at Scale
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Beschreibung
Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python.
Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on.As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling.
By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.
You will:
- Build an understanding of NLP problems in industry
- Gain the know-how to solve a typical NLP problem using language-based models and machine learning
- Discover the best methods to solve a business problem using NLP - the tried and tested ones
- Understand the business problems that are tough to solve
Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python.
Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover theproblems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling.
By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.
What You Will Learn
- Build an understanding of NLP problems in industry
- Gain the know-how to solve a typical NLP problem using language-based models and machine learning
- Discover the best methods to solve a business problem using NLP - the tried and tested ones
- Understand the business problems that are tough to solve
Who This Book Is For
Analytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve theproblems at hand.
Emphasizes a data- and business problem-first approach A case study-based approach that presents real-world problems and solutions Explains the accuracy and limitations of certain libraries from a professional's view
Autor*in
Mathangi Sri
Themen in »Practical Natural Language Processing with Python«
Natural Language Processing Python Deep Learning Recommender System Recurrent Neural Networks Automobile Finance Banking
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“Each of the book’s four chapters describes multiple approaches to the area of analysis, from simple or “classic” methods to more complex ML-based solutions. … Sri’s contribution fills that instructional gap with relevant and usable Python code examples.” (Harry J. Foxwell, Computing Reviews, November 9, 2021)
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