Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
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03 Sep 2009TL;DR: It is argued that query-drift can potentially be estimated by measuring the diversity of the retrieval scores of top-retrieved documents, and the prediction success is better, over most tested TREC corpora, than that of state-of-the-art prediction methods.
Abstract: Predicting query performance , that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. Our novel approach to addressing this challenge is based on estimating the potential amount of query drift in the result list, i.e., the presence (and dominance) of aspects or topics not related to the query in top-retrieved documents. We argue that query-drift can potentially be estimated by measuring the diversity (e.g., standard deviation) of the retrieval scores of these documents. Empirical evaluation demonstrates the prediction effectiveness of our approach for several retrieval models. Specifically, the prediction success is better, over most tested TREC corpora, than that of state-of-the-art prediction methods.
114 citations
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01 Nov 2002TL;DR: The TREC-2002 Web Track moved away from non-Web relevance ranking and towards Webspecific tasks on a 1.25 million page crawl “.GOV”, the topic distillation task involved finding pages which were relevant, but also had characteristics which would make them desirable inclusions in a distilled list of key pages.
Abstract: The TREC-2002 Web Track moved away from non-Web relevance ranking and towards Webspecific tasks on a 125 million page crawl “GOV” The topic distillation task involved finding pages which were relevant, but also had characteristics which would make them desirable inclusions in a distilled list of key pages The named page task is a variant of last year’s homepage finding task The task is to find a particular page, but in this year’s task the page need not be a home page
114 citations
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01 Dec 2009
TL;DR: In this paper, ranking and selecting entities based on reputation or influence scores is provided, where a method includes determining whether a first entity is a subject or an object; determining whether another entity is an object or a subject; and generating a graph, in which the graph includes directed and undirected links.
Abstract: Ranking and selecting entities based on calculated reputation or influence scores is provided. In some embodiments, a method includes determining whether a first entity is a subject or an object; determining whether a second entity is a subject or an object; and generating a graph, in which a subset of the graph is a subject graph of subject nodes that includes at least one or more subjects (e.g., subject entities) linked to one or more other subjects, and in which the graph includes one or more objects (e.g., object entities) each linked to one or more subjects in the subject graph. In some embodiments, the graph includes directed and undirected links. In some embodiments, the graph includes one or more objects linked to one or more objects.
114 citations
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30 Jun 2011TL;DR: In this paper, a system performs cross-language query translations by locating documents in the first language that contain references that match the terms of the search query and identifying documents in second language.
Abstract: A system performs cross-language query translations. The system receives a search query that includes terms in a first language and determines possible translations of the terms of the search query into a second language. The system also locates documents for use as parallel corpora to aid in the translation by: (1) locating documents in the first language that contain references that match the terms of the search query and identify documents in the second language; (2) locating documents in the first language that contain references that match the terms of the query and refer to other documents in the first language and identify documents in the second language that contain references to the other documents; or (3) locating documents in the first language that match the terms of the query and identify documents in the second language that contain references to the documents in the first language. The system may use the second language documents as parallel corpora to disambiguate among the possible translations of the terms of the search query and identify one of the possible translations as a likely translation of the search query into the second language.
114 citations
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11 Jul 2010TL;DR: A syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion is presented and substantially outperforms previous work on a paraphrase ranking task and achieves promising results on a wordsense similarity task.
Abstract: We present a syntactically enriched vector model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It employs a systematic combination of first- and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previous work on a paraphrase ranking task, and (ii) achieves promising results on a wordsense similarity task; to our knowledge, it is the first time that an unsupervised method has been applied to this task.
114 citations