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Practical NLP

A collection of notes learned from the book Practical Natural Language Processing. You can find original jupyter notebook of this book in this repository.

Notes Structure

  • Chapter 02: NLP Pipeline
  • Chapter 03: Text Representation
  • Chapter 04: Text Classification
    • One Pipeline Many Classifiers
    • Word Embeddings
    • Deep Learning for Text Classification
    • Text Classification with Large Pre-Trained Language Model
    • Lime and SHAP
    • Learning with No or Less Data with Case Study
  • Chapter 05: Information Extraction
    • Information Extraction
    • Keyphrase Extraction (KPE)
    • Named Entity Recognition (NER)
    • Named Entity Disambiguation and Linking
    • Relationship Extraction
    • Other Advanced IE Tasks and Case Study
  • Chapter 06: Chatbots
  • Chapter 07: Topics in Brief
    • Search and Information Retrieval (Elastic Search)
    • Topic Modeling
    • Text Summarization
    • Recommender Systems
    • Machine Translation
    • Question Answering Systems
  • Chapter 08: Social Media
  • Chapter 09: E-Commerce and Retail
  • Chapter 10: Healthcare, Finance, and Law
    • Heatlhcare
    • Finance and Law
  • Chapter 11: The End-to-End NLP Process

Practical NLP

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