Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP, 2nd Edition

Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP, 2nd Edition
Natural Language Processing with Java: Techniques for building machine learning and neural network models for NLP, 2nd Edition by Richard M Reese, AshishSingh Bhatia
English | 2018 | ISBN: 1788993494 | 318 Pages | True PDF, EPUB | 22 MB

Natural Language Processing with Java – Second Edition: Advanced machine learning and neural networks for building NLP applications
Explore various approaches to organize and extract useful text from unstructured data using Java
Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.
You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet, . You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.
By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.
What You Will Learn

  • Understand basic NLP tasks and how they relate to one another
  • Discover and use the available tokenization engines
  • Apply search techniques to find people, as well as things, within a document
  • Construct solutions to identify parts of speech within sentences
  • Use parsers to extract relationships between elements of a document
  • Identify topics in a set of documents
  • Explore topic modeling from a document