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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.


Papers
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Proceedings ArticleDOI
11 Aug 2002
TL;DR: The new language modeling approach is shown to explain a number of practical facts of today's information retrieval systems that are not very well explained by the current state of information retrieval theory, including stop words, mandatory terms, coordination level ranking and retrieval using phrases.
Abstract: This paper follows a formal approach to information retrieval based on statistical language models. By introducing some simple reformulations of the basic language modeling approach we introduce the notion of importance of a query term. The importance of a query term is an unknown parameter that explicitly models which of the query terms are generated from the relevant documents (the important terms), and which are not (the unimportant terms). The new language modeling approach is shown to explain a number of practical facts of today's information retrieval systems that are not very well explained by the current state of information retrieval theory, including stop words, mandatory terms, coordination level ranking and retrieval using phrases.

107 citations

Patent
Jay Ponte1
30 Jul 1999
TL;DR: In this paper, a method and device for improving the quality of documents selected in response to a user query for documents such as Web pages or sites is presented, which involves the successive review by the user of a limited number of documents as being relevant or not relevant, the analysis of the characteristics of the documents so graded by means of information retrieval techniques, and the modification of the search query based upon that analysis, until the user is satisfied with the quality presented to him.
Abstract: Disclosed is a method and device for improving the quality of documents selected in response to a user query for documents such as Web pages or sites. The method is one of iteration, and involves the successive review by the user of a limited number of documents as being relevant or not relevant, the analysis of the characteristics of the documents so graded by means of information retrieval techniques, and the modification of the search query based upon that analysis, until the user is satisfied with the quality of the documents presented to him.

107 citations

Posted Content
TL;DR: Wang et al. as mentioned in this paper proposed a video span localizing network (VSLNet) to address NLVL task with a span-based QA approach by treating the input video as text passage.
Abstract: Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.

107 citations

Journal ArticleDOI
TL;DR: A new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the user's particular information needs.
Abstract: Content-based image retrieval systems require the development of relevance feedback mechanisms that allow the user to progressively refine the system's response to a query. In this paper a new relevance feedback mechanism is described which evaluates the feature distributions of the images judged relevant, or not relevant, by the user and dynamically updates both the similarity measure and the query in order to accurately represent the user's particular information needs. Experimental results demonstrate the effectiveness of this mechanism.

107 citations

Book ChapterDOI
01 Jun 2008
TL;DR: This work proposes a novel clustered-graph structure that corresponds to only a summary of the original ontology, and adopts several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements w.r.t. the query and the importance of ontological elements.
Abstract: The increasing amount of data on the Semantic Web offers opportunities for semantic search However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query's syntax or the underlying ontology Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries However, these approaches yet feature only very basic query ranking and do not scale to large repositories We tackle the scalability problem by proposing a novel clustered-graph structure that corresponds to only a summary of the original ontology The so reduced data space is then used in the exploration for the computation of top-k queries Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements wrt the query and the importance of ontology elements The experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes

107 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168