000 | 03617nam a22004695i 4500 | ||
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001 | 978-3-642-22898-8 | ||
003 | DE-He213 | ||
005 | 20140220083810.0 | ||
007 | cr nn 008mamaa | ||
008 | 110915s2011 gw | s |||| 0|eng d | ||
020 |
_a9783642228988 _9978-3-642-22898-8 |
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024 | 7 |
_a10.1007/978-3-642-22898-8 _2doi |
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050 | 4 | _aQA75.5-76.95 | |
072 | 7 |
_aUNH _2bicssc |
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072 | 7 |
_aUND _2bicssc |
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072 | 7 |
_aCOM030000 _2bisacsh |
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082 | 0 | 4 |
_a025.04 _223 |
100 | 1 |
_aMetzler, Donald. _eauthor. |
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245 | 1 | 2 |
_aA Feature-Centric View of Information Retrieval _h[electronic resource] / _cby Donald Metzler. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011. |
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300 |
_aXII, 168 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aThe Information Retrieval Series, _x1387-5264 ; _v27 |
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505 | 0 | _aIntroduction -- Classical Retrieval Models -- Feature-Based Ranking -- Feature-Based Query Expanion -- Query-Dependent Feature Weighting -- Model Learning. | |
520 | _aCommercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. As he shows, combining term dependencies and arbitrary features results in a very robust, powerful retrieval model. In addition, he describes several extensions, such as an automatic feature selection algorithm and a query expansion framework. The resulting model and extensions provide a flexible framework for highly effective retrieval across a wide range of tasks and data sets. A Feature-Centric View of Information Retrieval provides graduate students, as well as academic and industrial researchers in the fields of information retrieval and Web search with a modern perspective on information retrieval modeling and Web searches. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aInformation storage and retrieval systems. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aProbability and Statistics in Computer Science. |
650 | 2 | 4 | _aMathematical Applications in Computer Science. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642228971 |
830 | 0 |
_aThe Information Retrieval Series, _x1387-5264 ; _v27 |
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856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-22898-8 |
912 | _aZDB-2-SCS | ||
999 |
_c108277 _d108277 |