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

Chapter 8. Classifying Texts and Documents

In this chapter, we will demonstrate how to use various Natural Language Processing (NLP) APIs to perform text classification. This is not to be confused with text clustering. Clustering is concerned with the identification of text without the use of predefined categories. Classification, in contrast, uses predefined categories. In this chapter, we will focus on text classification, where tags are assigned to text to specify its type.

The general approach that is used to perform text classification starts with the training of a model. The model is validated and then used to classify documents. We will focus on the training and usage stages of this process.

Documents can be classified according to any number of attributes, such as their subject, document type, time of publication, author, language used, and reading level. Some classification approaches require humans to label sample data.

Sentiment analysis is a type of classification. It is concerned...