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Natural language processing with Java and LingPipe cookbook : over 60 effective recipes to develop your natural language processing (NLP) skills quickly and effectively /
Annotation
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
[2014]
|
| Colección: | Quick answers to common problems.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Simple Classifiers; Introduction; Deserializing and running a classifier; Getting confidence estimates from a classifier; Getting data from the Twitter API; Applying a classifier to a .csv file; Evaluation of classifiers
- the confusion matrix; Training your own language model classifier; How to train and evaluate with cross validation; Viewing error categories
- false positives; Understanding precision and recall; How to serialize a LingPipe object
- classifier example
- Eliminate near duplicates with the Jaccard distanceHow to classify sentiment
- simple version; Chapter 2: Finding and Working with Words; Introduction; Introduction to tokenizer factories
- finding words in a character stream; Combining tokenizers
- lowercase tokenizer; Combining tokenizers
- stop word tokenizers; Using Lucene/Solr tokenizers; Using Lucene/Solr tokenizers with LingPipe; Evaluating tokenizers with unit tests; Modifying tokenizer factories; Finding words for languages without white spaces; Chapter 3: Advanced Classifiers; Introduction; A simple classifier
- Language model classifier with tokensNaïve Bayes; Feature extractors; Logistic regression; Multithreaded cross validation; Tuning parameters in logistic regression; Customizing feature extraction; Combining feature extractors; Classifier-building life cycle; Linguistic tuning; Thresholding classifiers; Train a little, learn a little
- active learning; Annotation; Chapter 4: Tagging Words and Tokens; Introduction; Interesting phrase detection; Foreground- or background-driven interesting phrase detection; Hidden Markov Models (HMM)
- part-of-speech; N-best word tagging
- Confidence-based taggingTraining word tagging; Word-tagging evaluation; Conditional random fields (CRF) for word/token tagging; Modifying CRFs; Chapter 5: Finding Spans in Text
- Chunking; Introduction; Sentence detection; Evaluation of sentence detection; Tuning sentence detection; Marking embedded chunks in a string
- sentence chunk example; Paragraph detection; Simple noun phrases and verb phrases; Regular expression-based chunking for NER; Dictionary-based chunking for NER; Translating between word tagging and chunks
- BIO codec; HMM-based NER; Mixing the NER sources; CRFs for chunking
- NER using CRFs with better featuresChapter 6: String Comparison and Clustering; Introduction; Distance and proximity
- simple edit distance; Weighted edit distance; The Jaccard distance; The Tf-Idf distance; Using edit distance and language models for spelling correction; The case restoring corrector; Automatic phrase completion; Single-link and complete-link clustering using edit distance; Latent Dirichlet allocation (LDA) for multitopic clustering; Chapter 7: Finding Coreference Between Concepts/People; Introduction; Named entity coreference with a document; Adding pronouns to coreference