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  • Book Overview & Buying Natural Language Processing with Java
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Natural Language Processing with Java

Natural Language Processing with Java

By : Richard M. Reese , Richard M Reese
4.6 (7)
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Natural Language Processing with Java

Natural Language Processing with Java

4.6 (7)
By: Richard M. Reese , Richard M Reese
Buy this Book

Overview of this book

If you are a Java programmer who wants to learn about the fundamental tasks underlying natural language processing, this book is for you. You will be able to identify and use NLP tasks for many common problems, and integrate them in your applications to solve more difficult problems. Readers should be familiar/experienced with Java software development.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to NLP
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1. Introduction to NLP
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What is NLP?
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Why use NLP?
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Why is NLP so hard?
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Survey of NLP tools
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Overview of text processing tasks
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Understanding NLP models
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Preparing data
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Summary
2
2. Finding Parts of Text
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2. Finding Parts of Text
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Understanding the parts of text
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What is tokenization?
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Simple Java tokenizers
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NLP tokenizer APIs
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Understanding normalization
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Summary
3
3. Finding Sentences
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3. Finding Sentences
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The SBD process
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What makes SBD difficult?
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Understanding SBD rules of LingPipe's HeuristicSentenceModel class
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Simple Java SBDs
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Using NLP APIs
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Training a Sentence Detector model
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Summary
4
4. Finding People and Things
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4. Finding People and Things
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Why NER is difficult?
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Techniques for name recognition
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Using regular expressions for NER
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Using NLP APIs
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Training a model
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Summary
5
5. Detecting Part of Speech
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5. Detecting Part of Speech
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The tagging process
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Using the NLP APIs
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Summary
6
6. Classifying Texts and Documents
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6. Classifying Texts and Documents
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How classification is used
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Understanding sentiment analysis
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Text classifying techniques
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Using APIs to classify text
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Summary
7
7. Using Parser to Extract Relationships
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7. Using Parser to Extract Relationships
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Relationship types
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Understanding parse trees
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Using extracted relationships
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Extracting relationships
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Using NLP APIs
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Extracting relationships for a question-answer system
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Summary
8
8. Combined Approaches
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8. Combined Approaches
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Preparing data
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Pipelines
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Creating a pipeline to search text
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Summary
9
Index
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Index

Using APIs to classify text

We will use OpenNLP, Stanford API, and LingPipe to demonstrate various classification approaches. We will spend more time with LingPipe as it offers several different classification approaches.

Using OpenNLP

The DocumentCategorizer interface specifies methods to support the classification process. The interface is implemented by the DocumentCategorizerME class. This class will classify text into predefined categories using a maximum entropy framework. We will:

  • Demonstrate how to train the model

  • Illustrate how the model can be used

Training an OpenNLP classification model

First, we have to train our model because OpenNLP does not have prebuilt models. This process consists of creating a file of training data and then using the DocumentCategorizerME model to perform the actual training. The model created is typically saved in a file for later use.

The training file format consists of a series of lines where each line represents a document. The first word of the line is...

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Natural Language Processing with Java
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