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Natural Language Processing with Java : Techniques for Building Machine Learning and Neural Network Models for NLP, 2nd Edition.
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 elem...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a M. Reese, Richard. | |
| 245 | 1 | 0 | |a Natural Language Processing with Java : |b Techniques for Building Machine Learning and Neural Network Models for NLP, 2nd Edition. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (308 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a Using LingPipe's named entity models. | ||
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Natural language processing. | |
| 650 | 0 | |a Java. | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Programming Languages |x Java. |2 bisacsh | |
| 650 | 7 | |a Java (Computer program language) |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Natural language processing (Computer science) |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 700 | 1 | |a Bhatia, AshishSingh. | |
| 758 | |i has work: |a Natural language processing with Java (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGHYtcFCwJBCbB4WbHYK8d |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a M. Reese, Richard. |t Natural Language Processing with Java : Techniques for Building Machine Learning and Neural Network Models for NLP, 2nd Edition. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788993494 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5485028 |z Texto completo |
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5485028 | ||
| 938 | |a YBP Library Services |b YANK |n 15625726 | ||
| 994 | |a 92 |b IZTAP | ||