دانلود کتاب Natural Language Processing with Java: Techniques for Building Machine Learning and Neural Network Models for NLP

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Cover
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Table of Contents
Preface
Chapter 1: Introduction to NLP
What is NLP?
Why use NLP?
Why is NLP so hard?
Survey of NLP tools
Apache OpenNLP
Stanford NLP
LingPipe
GATE
UIMA
Apache Lucene Core
Deep learning for Java
Overview of text-processing tasks
Finding parts of text
Finding sentences
Feature-engineering
Finding people and things
Detecting parts of speech
Classifying text and documents
Extracting relationships
Using combined approaches
Understanding NLP models
Identifying the task. Selecting a modelBuilding and training the model
Verifying the model
Using the model
Preparing data
Summary
Chapter 2: Finding Parts of Text
Understanding the parts of text
What is tokenization?
Uses of tokenizers
Simple Java tokenizers
Using the Scanner class
Specifying the delimiter
Using the split method
Using the BreakIterator class
Using the StreamTokenizer class
Using the StringTokenizer class
Performance considerations with Java core tokenization
NLP tokenizer APIs
Using the OpenNLPTokenizer class
Using the SimpleTokenizer class
Using the WhitespaceTokenizer class. Using the TokenizerME classUsing the Stanford tokenizer
Using the PTBTokenizer class
Using the DocumentPreprocessor class
Using a pipeline
Using LingPipe tokenizers
Training a tokenizer to find parts of text
Comparing tokenizers
Understanding normalization
Converting to lowercase
Removing stopwords
Creating a StopWords class
Using LingPipe to remove stopwords
Using stemming
Using the Porter Stemmer
Stemming with LingPipe
Using lemmatization
Using the StanfordLemmatizer class
Using lemmatization in OpenNLP
Normalizing using a pipeline
Summary
Chapter 3: Finding Sentences. The SBD processWhat makes SBD difficult?
Understanding the SBD rules of LingPipe's HeuristicSentenceModel class
Simple Java SBDs
Using regular expressions
Using the BreakIterator class
Using NLP APIs
Using OpenNLP
Using the SentenceDetectorME class
Using the sentPosDetect method
Using the Stanford API
Using the PTBTokenizer class
Using the DocumentPreprocessor class
Using the StanfordCoreNLP class
Using LingPipe
Using the IndoEuropeanSentenceModel class
Using the SentenceChunker class
Using the MedlineSentenceModel class
Training a sentence-detector model. Using the Trained modelEvaluating the model using the SentenceDetectorEvaluator class
Summary
Chapter 4: Finding People and Things
Why is NER difficult?
Techniques for name recognition
Lists and regular expressions
Statistical classifiers
Using regular expressions for NER
Using Java's regular expressions to find entities
Using the RegExChunker class of LingPipe
Using NLP APIs
Using OpenNLP for NER
Determining the accuracy of the entity
Using other entity types
Processing multiple entity types
Using the Stanford API for NER
Using LingPipe for NER.