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Natural Language Processing with Java

Natural Language Processing with Java

By : Richard M. Reese
2 (3)
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Natural Language Processing with Java

Natural Language Processing with Java

2 (3)
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 (14 chapters)
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Summary

In this chapter, we illustrated various approaches to tokenizing text and performing normalization on text. We started with simple tokenization techniques based on core Java classes, such as the String class' split method and the StringTokenizer class. These approaches can be useful when we decide to forgo the use of the NLP API classes.

We demonstrated how tokenization can be performed using the OpenNLP, Stanford, and LingPipe APIs. We found variations in how tokenization can be performed and options that can be applied in these APIs. A brief comparison of their output was provided.

Normalization was discussed, which can involve converting characters to lowercase, expanding abbreviations, removing stopwords, stemming, and lemmatization. We illustrated how these techniques can be applied using both core Java classes and the NLP APIs.

In the next chapter, Chapter...

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