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

Understanding the parts of text


There are a number of ways to categorize parts of text. For example, we may be concerned with character-level issues, such as punctuation, with a possible need to ignore or expand contractions. At the word level, we may need to perform different operations, such as the following:

  • Identifying morphemes using stemming and/or lemmatization
  • Expanding abbreviations and acronyms
  • Isolating number units

We cannot always split words with punctuation, because the punctuation is sometimes considered to be part of the word, such as the word can't. We may also be concerned with grouping multiple words to form meaningful phrases. Sentence-detection can also be a factor. We do not necessarily want to group words that cross sentence boundaries.

In this chapter, we are primarily concerned with the tokenization process and a few specialized techniques, such as stemming. We will not attempt to show how they are used in other NLP tasks. Those efforts are reserved for later chapters...