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)

Extracting Data for Use in NLP Analysis

Most NLP tasks are concerned with the analysis of data. In this chapter, we will illustrate several approaches to acquiring data from multiple sources. This includes processing data from an HTML page and PDF, Word, and Excel documents. Each of these techniques involves connecting to a data source and then extracting the data from that source. For complex documents, such as Wikipedia articles or a Word document, we will be faced with choices in terms of what type of data we want to retrieve.

For example, with an HTML document, we may be interested in the actual text and possibly the HTML markup. For a document containing a table of contents, we may want to process that information separately. To extract text form a Wikipedia article, we treat it as an HTML document.

These recipes are an introduction to the topic. Most of these data sources...