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

Preparing data


Text extraction is the primary phase for any NLP tasks you want to undertake. If given a blog post, we want to extract the content of the blog and want to find the title of the post, author of the post, date when the post is published, text or content of the post, media-like images, videos in the post, and links to other posts, if any. Text extraction includes the following:

  • Structuring so as to identify different fields, blocks of contents, and so on
  • Determining the language of the document
  • Finding the sentences, paragraphs, phrases, and quotes
  • Breaking the text in tokens so as to process it further
  • Normalization and tagging
  • Lemmatization and stemming so as to reduce the variations and come close to root words

It also helps in topic modeling, which we have covered in Chapter 9, Topic Modeling. Here, we will quickly cover how text extraction can be performed for HTML, Word, and PDF documents. Although there are several APIs that support these tasks, we will use the following:

  • Boilerpipe...