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Natural Language Processing with Java Cookbook

Natural Language Processing with Java Cookbook

By : Richard M. Reese, Richard M Reese
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Natural Language Processing with Java Cookbook

Natural Language Processing with Java Cookbook

By: Richard M. Reese, 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)
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Language Identification and Translation

Language processing is an important component of many applications. In this chapter, we will demonstrate how to determine the natural language in use, how to translate from one language to another, and how we can convert between text and speech.

There may be times when we are not sure which language we are dealing with. When this happens, we have several techniques that we can use. We will illustrate how this is done using LingPipe, Google, and Amazon libraries.

To translate between languages, we will use Google and Amazon; they both support a large number of languages and use a client/server approach. A client application will send a request to a server, which will respond with the translated text. These approaches require a bit more work than other approaches since we need to deal with communication and security.

Converting text to speech...

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