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 9. Topic Modeling

In this chapter, we will learn about the basics of topic modeling using a document that contains some text. The idea here is to get the topic from the text using certain available methods. This process falls under the category of text mining, and plays an important role in searching as well as clustering and organizing text. Today, it is used by many sites for recommendation purposes, such as when news sites recommend articles based on the topic of the article that is currently being read by the reader. This chapter covers the basics of topic modeling, including the basic concept of Latent Dirichlet Allocation (LDA). It will also show you how to use the MALLET package for topic modeling.

We will cover the following topics in this chapter:

  • What is topic modeling?
  • The basics of LDA
  • Topic modeling with MALLET