Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Information Retrieval Models: Foundations & Relationships

Information Retrieval Models: Foundations & Relationships

Roelleke, Thomas

Paperback

Series: Synthesis Lectures on Information Concepts, Retrieval, and S

Networking

ISBN10: 3031012003
ISBN13: 9783031012006
Publisher: Springer
Published: Jul 26 2013
Pages: 141
Weight: 0.65
Height: 0.35 Width: 7.50 Depth: 9.25
Language: English
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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