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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
06 Nov 2006
TL;DR: This work builds on recent advances in alternative differentiable pairwise cost functions, and shows that these techniques can be successfully applied to tuning the parameters of an existing family of IR scoring functions (BM25), in the sense that they cannot do better using sensible search heuristics that directly optimize the rank-based cost function NDCG.
Abstract: Optimising the parameters of ranking functions with respect to standard IR rank-dependent cost functions has eluded satisfactory analytical treatment. We build on recent advances in alternative differentiable pairwise cost functions, and show that these techniques can be successfully applied to tuning the parameters of an existing family of IR scoring functions (BM25), in the sense that we cannot do better using sensible search heuristics that directly optimize the rank-based cost function NDCG. We also demonstrate how the size of training set affects the number of parameters we can hope to tune this way.

125 citations

Proceedings ArticleDOI
05 Sep 2018
TL;DR: Several new models for document relevance ranking are explored, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016), and inspired by PACRR’s convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs.
Abstract: We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR’s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

125 citations

Patent
26 Mar 2002
TL;DR: In this paper, the authors present a system for resolving search queries to information providers in a distributed search network including nodes generating search requests and nodes providing information, where a node may operate as a hub to route search requests from requesting nodes to provider nodes.
Abstract: Systems and methods for resolving search queries to information providers in a distributing search network. In a network including nodes generating search requests and nodes providing information, a node may operate as hub to route search requests from requesting nodes to provider nodes. Providers may register with a network hub. Registration information may include address information and data indicating they queries or type of queries for which that provider may have relevant data. A hub may resolve search queries against provider registrations to determine a set of providers to which to route each search query. Several systems and methods of selecting some of the providers are described, including the use of bidding, ranking, and statistical data.

125 citations

01 Jan 2008
TL;DR: A new method to solve FFLP is proposed and a linear ranking function for defuzzifying the F FLP is used and equivalency between two problems is proved by some theorems.
Abstract: The modeling and solving the optimization problem is one of the most important daily problem.By notation the nature of data in practice which are imprecise, fully fuzzy linear programming problem (FFLP) is a power full tool to modeling the practical optimization problem. In This paper after introducing FFLP, a new method to solve it is proposed. a linear ranking function for defuzzifying the FFLP is used , Equivalency between two problems is proved by some theorems.

125 citations

Proceedings Article
02 Jun 2010
TL;DR: This paper proposes a learning to rank algorithm that effectively utilizes the relationship information among the candidates when ranking and achieves 18.5% improvement in terms of accuracy over the classification models for those entities which have corresponding entries in the Knowledge Base.
Abstract: This paper address the problem of entity linking Specifically, given an entity mentioned in unstructured texts, the task is to link this entity with an entry stored in the existing knowledge base This is an important task for information extraction It can serve as a convenient gateway to encyclopedic information, and can greatly improve the web users' experience Previous learning based solutions mainly focus on classification framework However, it's more suitable to consider it as a ranking problem In this paper, we propose a learning to rank algorithm for entity linking It effectively utilizes the relationship information among the candidates when ranking The experiment results on the TAC 2009 dataset demonstrate the effectiveness of our proposed framework The proposed method achieves 185% improvement in terms of accuracy over the classification models for those entities which have corresponding entries in the Knowledge Base The overall performance of the system is also better than that of the state-of-the-art methods

125 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