Information Retrieval Models
eBook - PDF

Information Retrieval Models

Foundations & Relationships

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Information Retrieval Models

Foundations & Relationships

About this book

Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-basedmodels. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index

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Yes, you can access Information Retrieval Models by Thomas Roelleke in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Networking. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Copyright Page
  3. Title Page
  4. Contents
  5. List of Figures
  6. Preface
  7. Acknowledgments
  8. Introduction
  9. Foundations of IR Models
  10. Relationships Between IR Models
  11. Summary & Research Outlook
  12. Bibliography
  13. Author's Biography
  14. Index