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001 978-3-642-22898-8
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008 110915s2011 gw | s |||| 0|eng d
020 _a9783642228988
_9978-3-642-22898-8
024 7 _a10.1007/978-3-642-22898-8
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aMetzler, Donald.
_eauthor.
245 1 2 _aA Feature-Centric View of Information Retrieval
_h[electronic resource] /
_cby Donald Metzler.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXII, 168 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Information Retrieval Series,
_x1387-5264 ;
_v27
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
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-22898-8
912 _aZDB-2-SCS
999 _c108277
_d108277