Natural Language Processing with Java 1st Edition by Richard M Reese – Ebook PDF Instant Download/Delivery: 1784391794, 978-1784391799
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Product details:
ISBN 10: 1784391794
ISBN 13: 978-1784391799
Author: Richard M Reese
Explore various approaches to organize and extract useful text from unstructured data using Java
About This Book
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Integrate basic tasks to tackle more complex NLP problems
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Train NLP models to address domain-specific problem areas
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Learn to use a variety of core NLP techniques with this pragmatic guide
Who This Book Is For
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.
What You Will Learn
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Develop a deep understanding of the basic NLP tasks and how they relate to each other
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Discover and use the available tokenization engines
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Implement techniques for end of sentence detection
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Apply search techniques to find people and things within a document
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Construct solutions to identify parts of speech within sentences
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Use parsers to extract relationships between elements of a document
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Integrate basic tasks to tackle more complex NLP problems
In Detail
Natural Language Processing (NLP) is an important area of application development and its relevance in addressing contemporary problems will only increase in the future. There has been a significant increase in the demand for natural language-accessible applications supported by NLP tasks.
Natural Language Processing with Java will explore how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. It covers concepts of NLP that even those of you without a background in statistics or natural language processing can understand.
Natural Language Processing with Java 1st Table of contents:
1. Introduction to Natural Language Processing (NLP)
- What is NLP?
- Why use NLP?
- Challenges in NLP
- Why is NLP difficult?
- Survey of NLP Tools
- Apache OpenNLP
- Stanford NLP
- LingPipe
- GATE
- UIMA
- Overview of Text Processing Tasks
- Finding parts of text
- Sentence segmentation
- Named entity recognition (NER)
- Part of Speech (POS) tagging
- Text classification
- Relationship extraction
- Combining approaches for better results
- Understanding NLP Models
- Identifying the task
- Selecting a model
- Building and training the model
- Verifying the model
- Using the model
- Preparing Data
- Summary
2. Finding Parts of Text
- Understanding Parts of Text
- Tokenization
- What is tokenization?
- Uses of tokenizers
- Tokenization Techniques in Java
- Using
Scanner
class - Specifying delimiters
- Using
split
method - Using
BreakIterator
- Using
StreamTokenizer
- Using
StringTokenizer
- Using
- Performance Considerations in Java Tokenization
- NLP Tokenizer APIs
- OpenNLP Tokenizer
- SimpleTokenizer
- WhitespaceTokenizer
- TokenizerME
- Stanford Tokenizer
- PTBTokenizer
- DocumentPreprocessor
- Tokenization Pipelines
- Using LingPipe Tokenizers
- Training a Tokenizer
- Normalization Techniques
- Converting to lowercase
- Removing stopwords
- Using LingPipe for stopwords removal
- Stemming (Porter Stemmer and LingPipe)
- Lemmatization (Stanford and OpenNLP)
- Summary
3. Finding Sentences
- Sentence Boundary Detection (SBD)
- The SBD process
- Challenges in SBD
- Rules of LingPipe’s
HeuristicSentenceModel
- Java-Based Sentence Detection
- Using regular expressions
- Using
BreakIterator
- NLP APIs for Sentence Detection
- OpenNLP:
SentenceDetectorME
- Stanford API
- LingPipe: IndoEuropeanSentenceModel
- OpenNLP:
- Training a Sentence Detection Model
- Evaluating using
SentenceDetectorEvaluator
- Evaluating using
- Summary
4. Finding People and Things (Named Entity Recognition)
- Challenges in Named Entity Recognition (NER)
- Techniques for NER
- Lists and regular expressions
- Statistical classifiers
- Using regular expressions with Java and LingPipe
- Using NLP APIs for NER
- OpenNLP for NER
- Stanford NER
- LingPipe NER models
- Training an NER model
- Entity Accuracy and Evaluation
- Processing Multiple Entity Types
- Summary
5. Detecting Part of Speech (POS)
- POS Tagging Process
- Importance of POS tagging
- Challenges in POS tagging
- Using NLP APIs for POS Tagging
- OpenNLP:
POSTaggerME
, Chunking, and Dictionary class - Stanford POS: MaxentTagger and pipeline
- LingPipe POS tagging:
HmmDecoder
with Best-First and NBest tags
- OpenNLP:
- Training a POS Model
- Summary
6. Classifying Texts and Documents
- Overview of Text Classification
- Sentiment analysis
- Text classification techniques
- Using APIs for Classification
- OpenNLP: Training and using
DocumentCategorizerME
- Stanford:
ColumnDataClassifier
for sentiment analysis - LingPipe: Text classification, sentiment analysis, and language identification
- OpenNLP: Training and using
- Summary
7. Using Parsers to Extract Relationships
- Understanding Parse Trees
- Types of Relationships
- Extracting Relationships Using NLP APIs
- OpenNLP and Stanford API
- LexicalizedParser and
TreePrint
classes - GrammaticalStructure for word dependencies
- Coreference resolution
- Applications in Question-Answer Systems
- Summary
8. Combined Approaches
- Data Preparation
- Using Boilerpipe for HTML extraction
- POI for Word document extraction
- PDFBox for PDF text extraction
- Building Pipelines
- Stanford NLP pipeline
- Using multiple cores in a pipeline
- Creating pipelines for text search
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