Cover image for Deep Learning for Search

Deep Learning for Search

Author Tommaso Teofili
Release Date: 2019/06/01
ISBN: 9781617294792
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Book Description

Deep Learning for Search teaches you to improve your search results with neural networks. You’ll review how DL relates to search basics like indexing and ranking. Then, you’ll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you’ll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn!

Table of Contents

  1. Copyright
  2. Brief Table of Contents
  3. Table of Contents
  4. Foreword
  5. Preface
  6. Acknowledgments
  7. About this book
  8. About the author
  9. About the cover illustration
  10. Part 1. Search meets deep learning
    1. Chapter 1. Neural search
      1. 1.1. Neural networks and deep learning
      2. 1.2. What is machine learning?
      3. 1.3. What deep learning can do for search
      4. 1.4. A roadmap for learning deep learning
      5. 1.5. Retrieving useful information
      6. 1.6. Unsolved problems
      7. 1.7. Opening the search engine black box
      8. 1.8. Deep learning to the rescue
      9. 1.9. Index, please meet neuron
      10. 1.10. Neural network training
      11. 1.11. The promises of neural search
      12. Summary
    2. Chapter 2. Generating synonyms
      1. 2.1. Introduction to synonym expansion
      2. 2.2. The importance of context
      3. 2.3. Feed-forward neural networks
      4. 2.4. Using word2vec
      5. 2.5. Evaluations and comparisons
      6. 2.6. Considerations for production systems
      7. Summary
  11. Part 2. Throwing neural nets at a search engine
    1. Chapter 3. From plain retrieval to text generation
      1. 3.1. Information need vs. query: Bridging the gap
      2. 3.2. Learning over sequences
      3. 3.3. Recurrent neural networks
      4. 3.4. LSTM networks for unsupervised text generation
      5. 3.5. From unsupervised to supervised text generation
      6. 3.6. Considerations for production systems
      7. Summary
    2. Chapter 4. More-sensitive query suggestions
      1. 4.1. Generating query suggestions
      2. 4.2. Lucene Lookup APIs
      3. 4.3. Analyzed suggesters
      4. 4.4. Using language models
      5. 4.5. Content-based suggesters
      6. 4.6. Neural language models
      7. 4.7. Character-based neural language model for suggestions
      8. 4.8. Tuning the LSTM language model
      9. 4.9. Diversifying suggestions using word embeddings
      10. Summary
    3. Chapter 5. Ranking search results with word embeddings
      1. 5.1. The importance of ranking
      2. 5.2. Retrieval models
      3. 5.3. Neural information retrieval
      4. 5.4. From word to document vectors
      5. 5.5. Evaluations and comparisons
      6. Summary
    4. Chapter 6. Document embeddings for rankings and recommendations
      1. 6.1. From word to document embeddings
      2. 6.2. Using paragraph vectors in ranking
      3. 6.3. Document embeddings and related content
      4. Summary
  12. Part 3. One step beyond
    1. Chapter 7. Searching across languages
      1. 7.1. Serving users who speak multiple languages
      2. 7.2. Statistical machine translation
      3. 7.3. Working with parallel corpora
      4. 7.4. Neural machine translation
      5. 7.5. Word and document embeddings for multiple languages
      6. Summary
    2. Chapter 8. Content-based image search
      1. 8.1. Image contents and search
      2. 8.2. A look back: Text-based image retrieval
      3. 8.3. Understanding images
      4. 8.4. Deep learning for image representation
      5. 8.5. Working with unlabeled images
      6. Summary
    3. Chapter 9. A peek at performance
      1. 9.1. Performance and the promises of deep learning
      2. 9.2. Indexes and neurons working together
      3. 9.3. Working with streams of data
      4. Summary
      5. Looking forward
  13. Index
  14. List of Figures
  15. List of Tables
  16. List of Listings
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