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 5. Detecting Part of Speech

Previously, we identified parts of text, such as people, places, and things. In this chapter, we will investigate the process of finding Part-Of-Speech (POS). These are the parts that we recognize in English as grammatical elements, such as nouns and verbs. We will find that the context of the word is an important aspect of determining what type of word it is.

We will examine the tagging process, which essentially assigns a POS to a tag. This process is at the heart of detecting POS. We will briefly discuss why tagging is important, and then examine the various factors that make detecting POS difficult. Various Natural Language Processing (NLP) APIs are then used to illustrate the tagging process. We will also demonstrate how to train a model to address specialized text.

We will cover the following topics in this chapter:

  • The tagging process
  • Using the NLP APIs