Book Image

Natural Language Processing with Java Cookbook

By : Richard M. Reese
Book Image

Natural Language Processing with Java Cookbook

By: Richard M. Reese

Overview of this book

Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon Web Services (AWS). You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentence, or semantic word.
Table of Contents (14 chapters)

Finding the distance between text

There are several ways to measure the distance between two strings. In this recipe, we will demonstrate how to use the Apache Commons Text library to compute the Hamming and Levenshtein distances.

Distance is concerned with the number of operations needed to convert one string into another string. These operations can be either single-character deletion, insertion, or substitution.

The Hamming distance algorithm works on strings of equal length, which may limit its utility in some situations. It simply measures the number of positions in the two strings that differ. It is case-sensitive.

The HammingDistance class possesses a single default constructor and an apply method, which takes two strings as its arguments. The method returns the distance between the strings.

A more detailed Hamming distance explanation can be found at http://en.wikipedia...