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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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What is topic modeling?


In very simple terms, topic modeling is a technique by which the computer programs try and extract a topic from the text. The text is usually unstructured data, such as a blog, email, article, a chapter from a book, or something similar. It is a text-mining approach, but should not be confused with rule-based text mining. In a machine learning scenario, topic modeling falls under the category of unsupervised learning, where the machine or computer program tries to find the topic by observing a bunch of words in the last collection of text. A good model should result in the words "program", "programmer", "IT", "computer", "software", and "hardware" when given the topic of "IT industry". It helps in making sense of large text, and plays a vital role in the operation of search engines.

Topic modeling can be used with methods to organize, categorize, understand, and summarize large collections of textual information. It enables us to discover hidden patterns in collections...

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