Practical Natural Language Processing

Book Description

If you want to build, iterate and scale NLP systems in a business setting and to tailor them for various industry verticals, this is your guide.

Consider the task of building a chatbot or text classification system at your organization. In the beginning, there may be little or no data to work with. At this point, a basic solution that uses rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated—and often data intensive—ML techniques can be used including deep learning. At each step of this journey, there are dozens of alternative approaches you can take. This book helps you navigate this maze of options.

Table of Contents

  1. Introduction
    1. Why We Wrote This Book
    2. The Philosophy
    3. What The Readers Will Learn
      1. Minimally Qualified Reader
    4. Structure of the Book
    5. Table of Contents
  2. 1. NLP: A Primer
    1. NLP in the Real-World
    2. Challenges in NLP
    3. NLP Pipeline
    4. An NLP Walkthrough: Conversational Agents
    5. Machine Learning, Deep Learning and NLP
    6. Summary
  3. 2. Text Classification
    1. A taxonomy of text classification
    2. Applications
    3. A Pipeline for Building Text Classification Systems
      1. A simple classifier without this pipeline
      2. Using existing text classification APIs
    4. One Pipeline, Many Classifiers
      1. Naive Bayes Classifier
      2. Logistic Regression
      3. Support Vector Machine (SVM)
      4. Using Neural Embeddings in Text Classification
      5. Word Embeddings
      6. Subword Embeddings and fastText
      7. Document Embeddings
    5. Deep Learning for Text Classification
      1. CNN for Text Classification
      2. LSTMs for text classification
    6. Learning with Less (or No) Data, and Adapting to New Domains
      1. No Training Data
      2. Less Training Data: Active Learning and Domain Adaptation
      3. A Case Study
    7. Practical Advice
    8. Summary
  4. 3. NLP in e-Commerce
    1. Search
    2. Building an e-commerce catalogue
    3. Review analysis
    4. Recommendation for e-commerce
    5. Search in E-Commerce
    6. Building an E-Commerce Catalogue
      1. Attribute Extraction
      2. Product Categorization and Taxonomy
      3. Importance of Taxonomy
      4. Product Enrichment
      5. Product Duplication and Matching
    7. Review Analysis
      1. Sentiment Analysis
      2. Aspect Level Sentiment analysis
      3. Connecting Overall Ratings to Aspects - Latent Rating Regression (LARA)
      4. Understanding Aspects in a better way
    8. Recommendations for e-Commerce
      1. Case Study - Substitutes and Complements
    9. Summary
    10. References

Product Information

  • Title: Practical Natural Language Processing
  • Author(s): Harshit Surana, Anuj Gupta, Bodhisattwa Majumder, Sowmya Vajjala
  • Release date: May 2020
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492054047