Natural language processing with Java : techniques for building machine learning and neural network models for NLP / Richard M. Reese, AshishSingh Bhatia.

Format:

Book

Author/Creator:

Reese, Richard M., author.

Bhatia, AshishSingh, author.

Language:

English

Subjects (All):

Natural language processing (Computer science).

Java (Computer program language).

Physical Description:

1 online resource (308 pages) : illustrations

Edition:

Second edition.

Place of Publication:

Birmingham ; Mumbai : Packt, 2018.

System Details:

text file

Biography/History:

M. Reese Richard: Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java

Summary:

Explore various approaches to organize and extract useful text from unstructured data using Java Key Features Use deep learning and NLP techniques in Java to discover hidden insights in text Work with popular Java libraries such as CoreNLP, OpenNLP, and Mallet Explore machine translation, identifying parts of speech, and topic modeling Book Description 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. What you will learn Understand basic NLP tasks and how they relate to one another Discover and use the available tokenization engines Apply search techniques to find people, as well as things, within a document Construct solutions to identify parts of speech within sentences Use parsers to extract relationships between elements of a document Identify topics in a set of documents Explore topic modeling from a document Who this book is for Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.

Contents:

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 model

Building 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 class

Using 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

Chapter 3: Finding Sentences

The SBD process

What makes SBD difficult?

Understanding the SBD rules of LingPipe's HeuristicSentenceModel class

Simple Java SBDs

Using regular expressions

Using NLP APIs

Using OpenNLP

Using the SentenceDetectorME class

Using the sentPosDetect method

Using the Stanford API

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 model

Evaluating the model using the SentenceDetectorEvaluator class

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 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

Using LingPipe's named entity models

Using the ExactDictionaryChunker class

Building a new dataset with the NER annotation tool

Training a model

Evaluating a model

Chapter 5: Detecting Part of Speech

The tagging process

The importance of POS taggers

What makes POS difficult?

Using the NLP APIs

Using OpenNLP POS taggers

Using the OpenNLP POSTaggerME class for POS taggers

Using OpenNLP chunking

Using the POSDictionary class

Obtaining the tag dictionary for a tagger

Determining a word's tags

Changing a word's tags.

Adding a new tag dictionary

Creating a dictionary from a file

Using Stanford POS taggers

Using Stanford MaxentTagger

Using the MaxentTagger class to tag textese

Using the Stanford pipeline to perform tagging

Using LingPipe POS taggers

Using the HmmDecoder class with Best_First tags

Using the HmmDecoder class with NBest tags

Determining tag confidence with the HmmDecoder class

Training the OpenNLP POSModel

Chapter 6: Representing Text with Features

N-grams

Word embedding

GloVe

Word2vec

Dimensionality reduction

Principle component analysis

Distributed stochastic neighbor embedding

Chapter 7: Information Retrieval

Boolean retrieval

Dictionaries and tolerant retrieval

Wildcard queries

Spelling correction

Soundex

Vector space model

Scoring and term weighting

Inverse document frequency

TF-IDF weighting

Evaluation of information retrieval systems

Chapter 8: Classifying Texts and Documents

How classification is used

Understanding sentiment analysis

Text-classifying techniques

Using APIs to classify text

Training an OpenNLP classification model

Using DocumentCategorizerME to classify text

Using the ColumnDataClassifier class for classification

Using the Stanford pipeline to perform sentiment analysis

Using LingPipe to classify text

Training text using the Classified class

Using other training categories

Classifying text using LingPipe

Sentiment analysis using LingPipe

Language identification using LingPipe

Chapter 9: Topic Modeling

What is topic modeling?

The basics of LDA

Topic modeling with MALLET

Training

Evaluation

Chapter 10: Using Parsers to Extract Relationships

Relationship types.

Understanding parse trees

Using extracted relationships

Using the LexicalizedParser class

Using the TreePrint class

Finding word dependencies using the GrammaticalStructure class

Finding coreference resolution entities

Extracting relationships for a question-answer system

Finding the word dependencies

Determining the question type

Searching for the answer

Chapter 11: Combined Pipeline

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

Chapter 12: Creating a Chatbot

Chatbot architecture

Artificial Linguistic Internet Computer Entity

Understanding AIML

Developing a chatbot using ALICE and AIML

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Index.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781788993067

1788993063

OCLC:

1048799933