| 000 | 03739nam a22004575i 4500 | ||
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| 001 | 978-3-031-02328-6 | ||
| 003 | DE-He213 | ||
| 005 | 20240730163903.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2013 sz | s |||| 0|eng d | ||
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_a9783031023286 _9978-3-031-02328-6 |
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| 024 | 7 |
_a10.1007/978-3-031-02328-6 _2doi |
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| 050 | 4 | _aTK5105.5-5105.9 | |
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_aUKN _2bicssc |
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_aCOM043000 _2bisacsh |
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_aUKN _2thema |
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_a004.6 _223 |
| 100 | 1 |
_aRoelleke, Thomas. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981048 |
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| 245 | 1 | 0 |
_aInformation Retrieval Models _h[electronic resource] : _bFoundations & Relationships / _cby Thomas Roelleke. |
| 250 | _a1st ed. 2013. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2013. |
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| 300 |
_aXXI, 141 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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_aSynthesis Lectures on Information Concepts, Retrieval, and Services, _x1947-9468 |
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| 505 | 0 | _aList of Figures -- Preface -- Acknowledgments -- Introduction -- Foundations of IR Models -- Relationships Between IR Models -- Summary & Research Outlook -- Bibliography -- Author's Biography -- Index. | |
| 520 | _aInformation 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. | ||
| 650 | 0 |
_aComputer networks . _931572 |
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| 650 | 1 | 4 |
_aComputer Communication Networks. _981049 |
| 710 | 2 |
_aSpringerLink (Online service) _981050 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031012006 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031034565 |
| 830 | 0 |
_aSynthesis Lectures on Information Concepts, Retrieval, and Services, _x1947-9468 _981051 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02328-6 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85094 _d85094 |
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