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

TF-IDF weighting


TF-IDF combines the approaches of term frequency (TF) and inverse document frequency (IDF) to generate a weight for each term in a document, and it is done using the following formula:

In other words, it assigns a weight to term t in document d as follows:

  • If term t occurs many times in a few documents, it will be the highest
  • If term t occurs a small number of times in a document, it will be lower
  • If term t occurs in all documents, it will be the lowest
  • If term t occurs in no documents, it will be 0