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Natural Language Processing with Java

Natural Language Processing with Java - Second Edition

By : Richard M. Reese
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Natural Language Processing with Java

Natural Language Processing with Java

2 (3)
By: Richard M. Reese

Overview of this book

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.
Table of Contents (14 chapters)
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Finding People and Things

The process of finding people and things is referred to as Named Entity Recognition (NER). Entities such as people and places are associated with categories that have names, which identify what they are. A named category can be as simple as people. Common entity types include the following:

  • People
  • Locations
  • Organizations
  • Money
  • Time
  • URLs

Finding names, locations, and various things in a document are important and useful NLP tasks. They are used in many places, such as conducting simple searches, processing queries, resolving references, the disambiguation of text, and finding the meaning of text. For example, NER is sometimes interested in only finding those entities that belong to a single category. Using categories, the search can be isolated to those item types. Other NLP tasks use NER, such as in Part-Of-Speech (POS) taggers and in performing cross...

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