Difference between revisions of "Novasearch at Trec 2013 Microblog Track: Experiments with Reranking Using Wikipedia"
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| − | '''Novasearch at Trec 2013 Microblog Track: Experiments with Reranking Using Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Flávio Martins, André Mourão and João Magalhães. | + | '''Novasearch at Trec 2013 Microblog Track: Experiments with Reranking Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Flávio Martins]], [[André Mourão]] and [[João Magalhães]]. |
== Overview == | == Overview == | ||
| − | Users engaged in microblogging services and social-media apps contribute to multiple real-time text streams which amass large volumes of messages often sparked by events reported in newswire and in other media. Authors explore the use of external sources to detect topic popularity surges and improve microblog search performance using timebased language models [3]. The major novelty concerns the analysis that explores Wikipedia page view streams to find topic interest spikes. Authors obtained promising initial results when using evidence from Wikipedia for temporal reranking with the Tweets2013 dataset. | + | Users engaged in microblogging services and social-media apps contribute to multiple real-time text streams which amass large volumes of messages often sparked by events reported in newswire and in other media. Authors explore the use of external sources to detect topic popularity surges and improve microblog search performance using timebased language models [3]. The major novelty concerns the analysis that explores [[Wikipedia]] page view streams to find topic interest spikes. Authors obtained promising initial results when using evidence from Wikipedia for temporal reranking with the Tweets2013 dataset. |
Revision as of 09:38, 16 October 2019
Novasearch at Trec 2013 Microblog Track: Experiments with Reranking Using Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Flávio Martins, André Mourão and João Magalhães.
Overview
Users engaged in microblogging services and social-media apps contribute to multiple real-time text streams which amass large volumes of messages often sparked by events reported in newswire and in other media. Authors explore the use of external sources to detect topic popularity surges and improve microblog search performance using timebased language models [3]. The major novelty concerns the analysis that explores Wikipedia page view streams to find topic interest spikes. Authors obtained promising initial results when using evidence from Wikipedia for temporal reranking with the Tweets2013 dataset.
