English | September 18th, 2018 | ISBN: 1788993497 | 318 Pages | EPUB (True/HQ) | 13.97 MB
Explore various approaches to organize and extract useful text from unstructured data using Java
Key Features
Use deep learning and NLP techniques in Java to discover hidden insights in text
Work with popular Java libraries such as CoreNLP, OpenNLP, and Mallet
Explore machine translation, identifying parts of speech, and topic modeling
Book Description
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.
What you will learn
Understand basic NLP tasks and how they relate to one another
Discover and use the available tokenization engines
Apply search techniques to find people, as well as things, within a document
Construct solutions to identify parts of speech within sentences
Use parsers to extract relationships between elements of a document
Identify topics in a set of documents
Explore topic modeling from a document
Who this book is for
Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.
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