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Natural language processing with Java : explore various approaches to organize and extract useful text from unstructured data using Java /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Reese, Richard M., |d 1953- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjH3V6dJgypwHvKbphdVJC | |
| 245 | 1 | 0 | |a Natural language processing with Java : |b explore various approaches to organize and extract useful text from unstructured data using Java / |c Richard M. Reese. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from cover page (Safari, viewed April 14, 2015). | |
| 500 | |a Includes index. | ||
| 520 | 8 | |a Annotation |b 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. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; 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; Overview of text processing tasks; Finding parts of text; Finding sentences; 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 | |
| 505 | 8 | |a Verifying the modelUsing the model; Preparing data; Summary; Chapter 2: Finding Parts of Text; Understanding the parts of text; What is tokenization?; Uses for 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; Java core tokenization performance considerations; NLP tokenizer APIs; Using the OpenNLPTokenizer; Using the SimpleTokenizer class; Using the WhitespaceTokenizer class; Using the TokenizerME class; Using the Stanford tokenizer | |
| 505 | 8 | |a Using the PTBTokenizer classUsing 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 process; What makes SBD difficult? | |
| 505 | 8 | |a Understanding SBD rules of LingPipe's HeuristicSentenceModel classSimple 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 model | |
| 505 | 8 | |a Evaluating the model using the SentenceDetectorEvaluator classSummary; Chapter 4: Finding People and Things; Why NER is 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 LingPipe's RegExChunker class; 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; Using LingPipe's name entity models | |
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 0 | |a Java (Computer program language) | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 6 | |a Java (Langage de programmation) | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x Java. |2 bisacsh | |
| 650 | 7 | |a Java (Computer program language) |2 fast | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 758 | |i has work: |a NATURAL LANGUAGE PROCESSING WITH JAVA - (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYcXMVkVGBGdmtTDbJGyh3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Reese, Richard M. |t Natural language processing with java : explore various approaches to organize and extract useful text from unstructured data using java. |d Birmingham, England ; Mumbai, [India] : Packt Publishing, ©2015 |h xi, 237 pages |k Community experience distilled. |z 9781784391799 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=2000938 |z Texto completo |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781784391799/?ar |z Texto completo |
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