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Title Natural language processing and computational linguistics: a practical guide to text analysis with Python, Gensim, spaCy, and Keras
Creators Srinivasa-Desikan Bhargav
Collection Электронные книги зарубежных издательств ; Общая коллекция
Subjects Natural language processing (Computer science) ; Computational linguistics. ; Machine learning. ; Python (Computer program language) ; COMPUTERS / General ; EBSCO eBooks
Document type Other
File type PDF
Language English
Rights Доступ по паролю из сети Интернет (чтение, печать, копирование)
Record key on1046682463
Record create date 7/31/2018

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  • Cover
  • Copyright and Credits
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: What is Text Analysis?
    • What is text analysis?
    • Where's the data at?
    • Garbage in, garbage out
    • Why should you do text analysis?
    • Summary
    • References
  • Chapter 2: Python Tips for Text Analysis
    • Why Python?
    • Text manipulation in Python
    • Summary
    • References
  • Chapter 3: spaCy's Language Models
    • spaCy
    • Installation
      • Troubleshooting
      • Language models
      • Installing language models
      • Installation – how and why?
      • Basic preprocessing with language models
    • Tokenizing text
      • Part-of-speech (POS) – tagging
      • Named entity recognition
      • Rule-based matching
      • Preprocessing
    • Summary
    • References
  • Chapter 4: Gensim – Vectorizing Text and Transformations and n-grams
    • Introducing Gensim
    • Vectors and why we need them
      • Bag-of-words
      • TF-IDF
      • Other representations
    • Vector transformations in Gensim
    • n-grams and some more preprocessing
    • Summary
    • References
  • Chapter 5: POS-Tagging and Its Applications
    • What is POS-tagging?
    • POS-tagging in Python
      • POS-tagging with spaCy
    • Training our own POS-taggers
    • POS-tagging code examples
    • Summary
    • References
  • Chapter 6: NER-Tagging and Its Applications
    • What is NER-tagging?
    • NER-tagging in Python
      • NER-tagging with spaCy
    • Training our own NER-taggers
    • NER-tagging examples and visualization
    • Summary
    • References
  • Chapter 7: Dependency Parsing
    • Dependency parsing
    • Dependency parsing in Python
    • Dependency parsing with spaCy
    • Training our dependency parsers
    • Summary
    • References
  • Chapter 8: Topic Models
    • What are topic models?
    • Topic models in Gensim
      • Latent Dirichlet allocation
      • Latent semantic indexing
        • Hierarchical Dirichlet process
      • Dynamic topic models
    • Topic models in scikit-learn
    • Summary
    • References
  • Chapter 9: Advanced Topic Modeling
    • Advanced training tips
    • Exploring documents
    • Topic coherence and evaluating topic models
    • Visualizing topic models
    • Summary
    • References
  • Chapter 10: Clustering and Classifying Text
    • Clustering text
    • Starting clustering
    • K-means
    • Hierarchical clustering
    • Classifying text
    • Summary
    • References
  • Chapter 11: Similarity Queries and Summarization
    • Similarity metrics
    • Similarity queries
    • Summarizing text
    • Summary
    • References
  • Chapter 12: Word2Vec, Doc2Vec, and Gensim
    • Word2Vec
      • Using Word2Vec with Gensim
    • Doc2Vec
    • Other word embeddings
      • GloVe
      • FastText
      • WordRank
      • Varembed
      • Poincare
    • Summary
    • References
  • Chapter 13: Deep Learning for Text
    • Deep learning
    • Deep learning for text (and more)
    • Generating text
    • Summary
    • References
  • Chapter 14: Keras and spaCy for Deep Learning
    • Keras and spaCy
    • Classification with Keras
    • Classification with spaCy
    • Summary
    • References
  • Chapter 15: Sentiment Analysis and ChatBots
    • Sentiment analysis
      • Reddit for mining data
      • Twitter for mining data
    • ChatBots
    • Summary
    • References
  • Other Books You May Enjoy
  • Index
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