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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
01 Jul 2015
TL;DR: A simple, non-linear mention-ranking model for coreference resolution that attempts to learn distinct feature representations for anaphoricity detection and antecedent ranking, which is encouraged by pre-training on a pair of corresponding subtasks.
Abstract: We introduce a simple, non-linear mention-ranking model for coreference resolution that attempts to learn distinct feature representations for anaphoricity detection and antecedent ranking, which we encourage by pre-training on a pair of corresponding subtasks. Although we use only simple, unconjoined features, the model is able to learn useful representations, and we report the best overall score on the CoNLL 2012 English test set to date.

173 citations

Proceedings ArticleDOI
30 Mar 2004
TL;DR: This work proposes a novel way of integrating spatio-temporal indexes with sketches, traditionally used for approximate query processing, to solve the distinct counting problem of summarized information about moving objects that lie in a query region during a query interval.
Abstract: Several spatio-temporal applications require the retrieval of summarized information about moving objects that lie in a query region during a query interval (e.g., the number of mobile users covered by a cell, traffic volume in a district, etc.). Existing solutions have the distinct counting problem: if an object remains in the query region for several timestamps during the query interval, it will be counted multiple times in the result. We solve this problem by integrating spatio-temporal indexes with sketches, traditionally used for approximate query processing. The proposed techniques can also be applied to reduce the space requirements of conventional spatio-temporal data and to mine spatio-temporal association rules.

173 citations

Posted Content
TL;DR: Categorize and evaluate those algorithms proposed during the period of 2003 to 2016 for content-based image retrieval and conclude with several promising directions for future research.
Abstract: The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.

172 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: Experimental results show that query-expansion using document summaries can be considerably more effective than using full-document expansion and a novel approach to term-selection that separates the choice of relevant documents from the selection of a pool of potential expansion terms is presented.
Abstract: Query-expansion is an effective Relevance Feedback technique for improving performance in Information Retrieval. In general query-expansion methods select terms from the complete contents of relevant documents. One problem with this approach is that expansion terms unrelated to document relevance can be introduced into the modified query due to their presence in the relevant documents and distribution in the document collection. Motivated by the hypothesis that query-expansion terms should only be sought from the most relevant areas of a document, this investigation explores the use of document summaries in query-expansion. The investigation explores the use of both context-independent standard summaries and query-biased summaries. Experimental results using the Okapi BM25 probabilistic retrieval model with the TREC-8 ad hoc retrieval task show that query-expansion using document summaries can be considerably more effective than using full-document expansion. The paper also presents a novel approach to term-selection that separates the choice of relevant documents from the selection of a pool of potential expansion terms. Again, this technique is shown to be more effective that standard methods.

172 citations

Proceedings ArticleDOI
20 Jul 2008
TL;DR: A value profile based approach for ranking program statements according to their likelihood of being faulty, which outperforms Tarantula which is the most effective prior approach for statement ranking based fault localization using the benchmark programs the authors studied.
Abstract: We present a value profile based approach for ranking program statements according to their likelihood of being faulty. The key idea is to see which program statements exercised during a failing run use values that can be altered so that the execution instead produces correct output. Our approach is effective in locating statements that are either faulty or directly linked to a faulty statement. We present experimental results showing the effectiveness and efficiency of our approach. Our approach outperforms Tarantula which, to our knowledge, is the most effective prior approach for statement ranking based fault localization using the benchmark programs we studied.

172 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