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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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Journal ArticleDOI
01 Apr 1998
TL;DR: It is demonstrated that it is surprisingly difficult to identify which techniques work best, and comment on the experimental methodology required to support any claims as to the superiority of one method over another.
Abstract: Ranked queries are used to locate relevant documents in text databases In a ranked query a list of terms is specified, then the documents that most closely match the query are returned---in decreasing order of similarity---as answers Crucial to the efficacy of ranked querying is the use of a similarity heuristic, a mechanism that assigns a numeric score indicating how closely a document and the query match In this note we explore and categorise a range of similarity heuristics described in the literature We have implemented all of these measures in a structured way, and have carried out retrieval experiments with a substantial subset of these measuresOur purpose with this work is threefold: first, in enumerating the various measures in an orthogonal framework we make it straightforward for other researchers to describe and discuss similarity measures; second, by experimenting with a wide range of the measures, we hope to observe which features yield good retrieval behaviour in a variety of retrieval environments; and third, by describing our results so far, to gather feedback on the issues we have uncovered We demonstrate that it is surprisingly difficult to identify which techniques work best, and comment on the experimental methodology required to support any claims as to the superiority of one method over another

416 citations

Proceedings Article
15 Jul 2010
TL;DR: The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones and the overall ranking of the submitted systems is presented.
Abstract: This paper describes Task 5 of the Workshop on Semantic Evaluation 2010 (SemEval-2010). Systems are to automatically assign keyphrases or keywords to given scientific articles. The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones. We present the overall ranking of the submitted systems and discuss our findings to suggest future directions for this task.

413 citations

Proceedings ArticleDOI
07 Aug 2017
TL;DR: In this paper, a game theoretical minimax game is proposed to iteratively optimise both generative and discriminative models for document ranking, and the generative model is trained to fit the relevance distribution over documents via the signals from the discriminator.
Abstract: This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.

413 citations

Patent
05 Jul 1996
TL;DR: In this article, a method and apparatus for generating responses to queries to a document retrieval system is presented, which responds to a specific request for information by locating and ranking portions of text that may contain the information sought.
Abstract: The present invention relates to a method and apparatus for generating responses to queries to a document retrieval system. The system responds to a specific request for information by locating and ranking portions of text that may contain the information sought. It locates small relevant passages of text (called "hit passages") and ranks them according to an estimate of the degree to which they correspond to the information sought. The system minimizes the number of these hit passages that need to be examined before an information seeker has either found the desired information or can safely conclude that the information sought is not in the collection of texts. A relaxation ranking mechanism is provided to accommodate paraphrase variations that occur between the description of the information sought and the content of the text passages that may constitute suitable answers, by retrieving phrases that are dissimilar to the query phrase to different degrees according to a predefined set of rules, and penalizing the retrieved phrases based upon the degree of this dissimilarity, thus providing the user with a priority organized query hit list.

411 citations

Patent
28 Dec 2004
TL;DR: In this article, the authors present methods, systems, and computer-readable media for advanced computer file organization, computer file and web search and information retrieval, and intelligent assistant agent to assist a user's creative activities.
Abstract: The present invention presents embodiments of methods, systems, and computer-readable media for advanced computer file organization, computer file and web search and information retrieval, and intelligent assistant agent to assist a user's creative activities. The embodiments presented herein categorize search results based on the keywords used in the search, provide user selectable ranking, use user's search objectives and advices to refine search, conduct search within an application program and using a file based, provide always-on search that monitors changes over a period of time, provide a high level file system that organizes files into categories, according to relations among files, and in ranking orders along multiple categorization and ranking dimensions and multiple levels of conceptual relationships, conduct searches for associations between keywords, concepts, and propositions, and provide validations of such associations to assist a user's creative activity.

411 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