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標(biāo)題: Titlebook: A Feature-Centric View of Information Retrieval; Donald Metzler Book 2011 Springer-Verlag Berlin Heidelberg 2011 Markov models.Web queries [打印本頁(yè)]

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Model Learning,on the epoch, emphasis is laid on economic or social elements, on the control of trade routes, access to water or energy or on religious factors. This emphasis is often a mirror image of the fears of each historic period. As regards more ancient eras whose wars are related in literary form in great
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Book 2011 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.
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Introduction, it all in stride. Citizen attitudes in Greece are generally pessimistic, but it is only one point higher than that of the EU average as shown in the most recent Eurobarometer.. However, one cannot dismiss the glumness regarding the future that permeates the population as a whole.. Looking at the hi
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Classical Retrieval Models, Treaty Organization (NATO). Cooperation with and integration into the EU and NATO subsequently became the central foreign policy objectives of most of the CEECs. As a consequence, the “integration divide” between the “West” and the “East” of Eastern Europe came into existence.
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Feature-Based Query Expansion,tion ‘immigrant’ who held dual Dutch-Moroccan nationality and was convicted of the murder in 2005, had spent his whole life in the Netherlands and yet, according to his confession to the murder, renounced the culture of Dutch society and its democracy. Whether or not failures of integration had anyt
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Aid and Power in the Arab Worldls that go beyond the bag of words assumption. The bag of words models covered include the binary independence retrieval model, the 2-Poisson model, the BM25 model, unigram language models, and several other bag of words models. The models covered that go beyond the bag of words assumption include n
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https://doi.org/10.1057/9781137023483CE). The approach has three key benefits, including the ability to go beyond the bag of words assumption, the ability to employ arbitrary features during the query expansion process, and the ability to expand with a variety of concept types beyond unigrams. In addition to the basic LCE model, the ch
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Aid for Trade for Sustainable Developmentthe importance of query term concepts in a way that directly optimizes an underlying retrieval metric. By implementing concept weighting directly into the underlying retrieval model it avoids the issue of metric divergence. The chapter concludes with a rigorous experimental evaluation that demonstra
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Philippe De Lombaerde,George Mavrotasdescribing a number of techniques to estimate the model parameters in such a way that the resulting ranking functions are optimized for a target retrieval metric. The section describes a number of numerical analysis-based approaches, as well as a number of more sophisticated machine learning-inspire
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A Feature-Centric View of Information Retrieval978-3-642-22898-8Series ISSN 1871-7500 Series E-ISSN 2730-6836
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https://doi.org/10.1007/978-3-642-22898-8Markov models; Web queries; information retrieval; learning to rank; model mearing; search engines; “Bag o
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作者: Grievance    時(shí)間: 2025-3-25 08:52
Donald MetzlerPresents a novel paradigm for Web search, which is especially applicable to large data sets.Combines experiences from the author’s academic and industrial research over several years.Delivers the sing
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The Information Retrieval Serieshttp://image.papertrans.cn/a/image/140722.jpg
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Book 2011nement 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 co
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Aid and Power in the Arab Worldechnologies. The chapter then describes the importance of features, and how feature-based retrieval models came to arise as the result of a paradigm shift in information retrieval technology. The chapter then concludes by describing the desired characteristics that a robust, powerful feature-based retrieval framework should embody.
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Aid and Power in the Arab World-gram language models, the Indri inference network model, as well as several other previously proposed models. The chapter concludes with a discussion of the current state-of-the-art retrieval models, including their pros and cons.
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