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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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Topic modeling with MALLET


MALLET is a well-known library in topic modeling. It also supports document classification and sequence tagging. More about MALLET can be found at http://mallet.cs.umass.edu/index.php. To download MALLET, visit http://mallet.cs.umass.edu/download.php (the latest version is 2.0.6). Once downloaded, extract MALLET in the directory. It contains the sample data in .txt format in the sample-data/web/en path of the MALLET directory.

The first step is to import the files into MALLET's internal format. To do this, open the Command Prompt or Terminal, move to the mallet directory, and execute the following command:

mallet-2.0.6$ bin/mallet import-dir --input sample-data/web/en --output tutorial.mallet --keep-sequence --remove-stopwords

This command will generate the tutorial.mallet file.

Training

The next step is to use train-topics to build a topic model and save the output-state, topic-keys, and topics using the train-topics command:

mallet-2.0.6$ bin/mallet train-topics -...

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