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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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Patent
Kelly Wical1
21 May 1997
TL;DR: In this article, a knowledge base search and retrieval system, which includes factual knowledge base queries and concept knowledge base query, is disclosed, which stores associations among terminology/categories that have a lexical, semantical or usage association.
Abstract: A knowledge base search and retrieval system, which includes factual knowledge base queries and concept knowledge base queries, is disclosed. A knowledge base stores associations among terminology/categories that have a lexical, semantical or usage association. Document theme vectors identify the content of documents through themes as well as through classification of the documents in categories that reflects what the documents are primarily about. The factual knowledge base queries identify, in response to an input query, documents relevant to the input query through expansion of the query terms as well as through expansion of themes. The concept knowledge base query does not identify specific documents in response to a query, but specifies terminology that identifies the potential existence of documents in a particular area.

468 citations

Journal ArticleDOI
TL;DR: This paper systematically investigates the various problems and issues associated with the use of recall and precision as measures of retrieval system performance and provides a comparative analysis of methods available for defining precision in a probabilistic sense to promote a better understanding of the various issues involved in retrieval performance evaluation.
Abstract: Recall and precision are often used to evaluate the effectiveness of information retrieval systems. They are easy to define if there is a single query and if the retrieval result generated for the query is a linear ordering. However, when the retrieval results are weakly ordered, in the sense that several documents have an identical retrieval status value with respect to a query, some probabilistic notion of precision has to be introduced. Relevance probability, expected precision, and so forth, are some alternatives mentioned in the literature for this purpose. Furthermore, when many queries are to be evaluated and the retrieval results averaged over these queries, some method of interpolation of precision values at certain preselected recall levels is needed. The currently popular approaches for handling both a weak ordering and interpolation are found to be inconsistent, and the results obtained are not easy to interpret. Moreover, in cases where some alternatives are available, no comparative analysis that would facilitate the selection of a particular strategy has been provided. In this paper, we systematically investigate the various problems and issues associated with the use of recall and precision as measures of retrieval system performance. Our motivation is to provide a comparative analysis of methods available for defining precision in a probabilistic sense and to promote a better understanding of the various issues involved in retrieval performance evaluation.

464 citations

Proceedings ArticleDOI
26 Apr 2010
TL;DR: A novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query, is introduced and diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole.
Abstract: When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity.

464 citations

Book ChapterDOI
11 Jan 2004
TL;DR: In this article, a method for proving the termination of an unnested program loop by synthesizing linear ranking functions is presented, which relies on the fact that if a linear ranking function exists then it will be discovered by their method.
Abstract: We present an automated method for proving the termination of an unnested program loop by synthesizing linear ranking functions. The method is complete. Namely, if a linear ranking function exists then it will be discovered by our method. The method relies on the fact that we can obtain the linear ranking functions of the program loop as the solutions of a system of linear inequalities that we derive from the program loop. The method is used as a subroutine in a method for proving termination and other liveness properties of more general programs via transition invariants; see [PR03].

463 citations

Proceedings ArticleDOI
Yiming Yang1
01 Aug 1994
TL;DR: The simplicity of the model, the high recall-precision rates, and the efficient computation together make ExpNet preferable as a practical solution for real-world applications.
Abstract: Expert Network (ExpNet) is our new approach to automatic categorization and retrieval of natural language texts. We use a training set of texts with expert-assigned categories to construct a network which approximately reflects the conditional probabilities of categories given a text. The input nodes of the network are words in the training texts, the nodes on the intermediate level are the training texts, and the output nodes are categories. The links between nodes are computed based on statistics of the word distribution and the category distribution over the training set. ExpNet is used for relevance ranking of candidate categories of an arbitrary text in the case of text categorization, and for relevance ranking of documents via categories in the case of text retrieval. We have evaluated ExpNet in categorization and retrieval on a document collection of the MEDLINE database, and observed a performance in recall and precision comparable to the Linear Least Squares Fit (LLSF) mapping method, and significantly better than other methods tested. Computationally, ExpNet has an O(N 1og N) time complexity which is much more efficient than the cubic complexity of the LLSF method. The simplicity of the model, the high recall-precision rates, and the efficient computation together make ExpNet preferable as a practical solution for real-world applications.

457 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