Book Image

Natural Language Processing with Java - Second Edition

By : Richard M. Reese
Book Image

Natural Language Processing with Java - Second Edition

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 (19 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Using extracted relationships


Extracted relationships can be used for a number of purposes, including:

  • Building knowledge bases
  • Creating directories
  • Product searches
  • Patent analysis
  • Stock analysis
  • Intelligence analysis

An example of how relationships can be presented is illustrated by Wikipedia's infobox, as shown in the following screenshot. This infobox is for the entry Oklahoma and contains relationship types such as Official language, Capital, and details about its area:

There are many databases built using Wikipedia that extract relationships and information, such as:

  • Resource Description Framework (RDF): This uses triples such as Yosemite-location-California, where the location is the relation. This can be found at http://www.w3.org/RDF/.
  • DBpedia: This holds over one billion triples and is an example of a knowledge base created from Wikipedia. This can be found at https://wiki.dbpedia.org/about.

Another simple but interesting example is the infobox that is presented when a Google search of planet...