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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)
Buy this Book
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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 extracted relationships

Relationships extracted can be used for a number of purposes including:

  • Building knowledge bases

  • Creating directories

  • Product searches

  • Patent analysis

  • Stock analysis

  • Intelligence analysis

An example of how relationships can be presented is illustrated by Wikipedia's Infobox, as shown in the following figure. This Infobox is for the entry Oklahoma and contains relationships types such as official language, capital, and details about its area.

There are many databases built using Wikipedia that extract relationships and information such as:

  • Resource Description Framework (RDF): This uses triples such as Yosemite-location-California, where the location is the relation. This can be found at http://www.w3.org/RDF/.

  • DBPedia: This holds over one billion triples and is an example of a knowledge base created from Wikipedia. This can be found at http://dbpedia.org/About.

Another simple, but interesting example, is the Infobox that is presented when a Google search of "planet mercury...

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Tech Concepts
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Programming languages
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Natural Language Processing with Java
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