Difference between revisions of "Novasearch at Trec 2014 Microblog Track: Reranking with Wikipedia Page Views"
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| + | | title = Novasearch at Trec 2014 Microblog Track: Reranking with Wikipedia Page Views | ||
| + | | date = 2014 | ||
| + | | authors = [[Flávio Martins]]<br />[[João Magalhães]] | ||
| + | | link = http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA618826&Location=U2&doc=GetTRDoc.pdf | ||
| + | }} | ||
'''Novasearch at Trec 2014 Microblog Track: Reranking with Wikipedia Page Views''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Flávio Martins]] and [[João Magalhães]]. | '''Novasearch at Trec 2014 Microblog Track: Reranking with Wikipedia Page Views''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Flávio Martins]] and [[João Magalhães]]. | ||
== Overview == | == Overview == | ||
Abstract : This paper describes participation in the TREC 2014 Microblog real-time search task. Authors investigate whether page views from [[Wikipedia]] can be used successfully to estimate relevant time periods for queries. To this end, authors use a recently published temporal reranking method by Efron et al. [2], which uses kernel density estimation. | Abstract : This paper describes participation in the TREC 2014 Microblog real-time search task. Authors investigate whether page views from [[Wikipedia]] can be used successfully to estimate relevant time periods for queries. To this end, authors use a recently published temporal reranking method by Efron et al. [2], which uses kernel density estimation. | ||
Revision as of 07:39, 17 June 2019
Authors | Flávio Martins João Magalhães |
|---|---|
Publication date | 2014 |
Links | Original |
Novasearch at Trec 2014 Microblog Track: Reranking with Wikipedia Page Views - scientific work related to Wikipedia quality published in 2014, written by Flávio Martins and João Magalhães.
Overview
Abstract : This paper describes participation in the TREC 2014 Microblog real-time search task. Authors investigate whether page views from Wikipedia can be used successfully to estimate relevant time periods for queries. To this end, authors use a recently published temporal reranking method by Efron et al. [2], which uses kernel density estimation.
