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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
28 Oct 2007
TL;DR: This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connected the publications, as well as the authorship network that ties the previous two together.
Abstract: Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRankparadigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.

266 citations

Proceedings ArticleDOI
17 Jul 2006
TL;DR: It is shown that approximate inference in BAYESUM is possible on large data sets and results in a state-of-the-art summarization system, and how B Bayesian summarization can be understood as a justified query expansion technique in the language modeling for IR framework.
Abstract: We present BAYESUM (for "Bayesian summarization"), a model for sentence extraction in query-focused summarization. BAYESUM leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BAYESUM is not afflicted by the paucity of information in short queries. We show that approximate inference in BAYESUM is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BAYESUM can be understood as a justified query expansion technique in the language modeling for IR framework.

265 citations

Journal ArticleDOI
TL;DR: Algorithms are presented that run in time which depends nontrivially on the rank k of the element to be selected and which is sublinear with respect to set cardinality, and all bounds are shown to be optimal to within a constant multiplicative factor.

265 citations

Proceedings ArticleDOI
Hang Cui1, Renxu Sun1, Keya Li1, Min-Yen Kan1, Tat-Seng Chua1 
15 Aug 2005
TL;DR: This work presents two methods for learning relation mapping scores from past QA pairs: one based on mutual information and the other on expectation maximization, which significantly outperforms state-of-the-art density-based passage retrieval methods.
Abstract: State-of-the-art question answering (QA) systems employ term-density ranking to retrieve answer passages Such methods often retrieve incorrect passages as relationships among question terms are not considered Previous studies attempted to address this problem by matching dependency relations between questions and answers They used strict matching, which fails when semantically equivalent relationships are phrased differently We propose fuzzy relation matching based on statistical models We present two methods for learning relation mapping scores from past QA pairs: one based on mutual information and the other on expectation maximization Experimental results show that our method significantly outperforms state-of-the-art density-based passage retrieval methods by up to 78% in mean reciprocal rank Relation matching also brings about a 50% improvement in a system enhanced by query expansion

264 citations

Proceedings ArticleDOI
06 Nov 2006
TL;DR: This paper proposes a novel approach for predicting and ranking candidate expertise with respect to a query, and demonstrates that applying field-based weighting models improves the ranking of candidates.
Abstract: In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.

264 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