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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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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to NLP
Introduction to NLP
What is NLP?
Why use NLP?
Why is NLP so hard?
Survey of NLP tools
Deep learning for Java
Overview of text-processing tasks
Understanding NLP models
Preparing data
Summary
2
Finding Parts of Text
Finding Parts of Text
Understanding the parts of text
What is tokenization?
Simple Java tokenizers
NLP tokenizer APIs
Understanding normalization
Summary
3
Finding Sentences
Finding Sentences
The SBD process
What makes SBD difficult?
Understanding the SBD rules of LingPipe's HeuristicSentenceModel class
Simple Java SBDs
Using NLP APIs
Training a sentence-detector model
Summary
4
Finding People and Things
Finding People and Things
Why is NER difficult?
Techniques for name recognition
Using regular expressions for NER
Using NLP APIs
Building a new dataset with the NER annotation tool
Training a model
Summary
5
Detecting Part of Speech
Detecting Part of Speech
The tagging process
Using the NLP APIs
Summary
6
Representing Text with Features
Representing Text with Features
N-grams
Word embedding
GloVe
Word2vec
Dimensionality reduction
Principle component analysis
Distributed stochastic neighbor embedding
Summary
7
Information Retrieval
Information Retrieval
Boolean retrieval
Dictionaries and tolerant retrieval
Vector space model
Scoring and term weighting
Inverse document frequency
TF-IDF weighting
Evaluation of information retrieval systems
Summary
8
Classifying Texts and Documents
Classifying Texts and Documents
How classification is used
Understanding sentiment analysis
Text-classifying techniques
Using APIs to classify text
Summary
9
Topic Modeling
Topic Modeling
What is topic modeling?
The basics of LDA
Topic modeling with MALLET
Summary
10
Using Parsers to Extract Relationships
Using Parsers to Extract Relationships
Relationship types
Understanding parse trees
Using extracted relationships
Extracting relationships
Using NLP APIs
Extracting relationships for a question-answer system
Summary
11
Combined Pipeline
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Combined Pipeline
Preparing data
Using boilerpipe to extract text from HTML
Using POI to extract text from Word documents
Using PDFBox to extract text from PDF documents
Using Apache Tika for content analysis and extraction
Pipelines
Using the Stanford pipeline
Using multiple cores with the Stanford pipeline
Creating a pipeline to search text
Summary
12
Creating a Chatbot
Creating a Chatbot
Chatbot architecture
Artificial Linguistic Internet Computer Entity
Summary
13
Other Books You May Enjoy
Other Books You May Enjoy
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Pipelines

A pipeline is nothing more than a sequence of operations where the output of one operation is used as the input to another operation. We have seen it used in several examples in previous chapters but they have been relatively short. In particular, we saw how the Stanford StanfordCoreNLP class, with its use of annotators objects, supports the concept of pipelines nicely. We will discuss this approach in the next section. One of the advantages of a pipeline, if structured properly, is that it allows the easy addition and removal of processing elements. For example, if one step of the pipeline converts a token to lowercase, then it is easy to simply remove this step, with the remaining elements of the pipeline left untouched. However, some pipelines are not always this flexible. One step may require a previous step in order to work properly. In a pipeline, such as the one supported by the StanfordCoreNLP class, the following set of annotators is needed to support POS processing:

 props...

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