Difference between revisions of "A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia"

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{{Infobox work
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| title = A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia
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| date = 2013
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| authors = [[Xiaoshi Yin]]<br />[[Jimmy Xiangji Huang]]<br />[[Zhoujun Li]]<br />[[Xiaofeng Zhou]]
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| doi = 10.1109/TKDE.2012.24
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| link = https://dl.acm.org/citation.cfm?id=2498755
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}}
 
'''A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Xiaoshi Yin]], [[Jimmy Xiangji Huang]], [[Zhoujun Li]] and [[Xiaofeng Zhou]].
 
'''A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Xiaoshi Yin]], [[Jimmy Xiangji Huang]], [[Zhoujun Li]] and [[Xiaofeng Zhou]].
  
 
== Overview ==
 
== Overview ==
 
In this paper, authors propose a survival modeling approach to promoting ranking diversity for biomedical [[information retrieval]]. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using [[Wikipedia]] to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, authors further evaluate approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that approach can achieve promising performance improvements.
 
In this paper, authors propose a survival modeling approach to promoting ranking diversity for biomedical [[information retrieval]]. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using [[Wikipedia]] to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, authors further evaluate approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that approach can achieve promising performance improvements.

Revision as of 09:49, 10 November 2019


A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia
Authors
Xiaoshi Yin
Jimmy Xiangji Huang
Zhoujun Li
Xiaofeng Zhou
Publication date
2013
DOI
10.1109/TKDE.2012.24
Links
Original

A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Xiaoshi Yin, Jimmy Xiangji Huang, Zhoujun Li and Xiaofeng Zhou.

Overview

In this paper, authors propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using Wikipedia to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, authors further evaluate approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that approach can achieve promising performance improvements.