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

Description

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. You will leverage the power of Java to extract relationships within different elements of text and documents.;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. 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.