Difference between revisions of "Supervised Query Modeling Using Wikipedia"
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| + | | title = Supervised Query Modeling Using Wikipedia | ||
| + | | date = 2010 | ||
| + | | authors = [[Edgar Meij]]<br />[[Maarten de Rijke]] | ||
| + | | doi = 10.1145/1835449.1835660 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=1835449.1835660 | ||
| + | }} | ||
'''Supervised Query Modeling Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Edgar Meij]] and [[Maarten de Rijke]]. | '''Supervised Query Modeling Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2010, written by [[Edgar Meij]] and [[Maarten de Rijke]]. | ||
== Overview == | == Overview == | ||
Authors use [[Wikipedia]] articles to semantically inform the generation of query models. To this end, authors apply supervised machine learning to automatically link queries to Wikipedia articles and sample terms from the linked articles to re-estimate the query model. On a recent large web corpus, authors observe substantial gains in terms of both traditional metrics and diversity [[measures]]. | Authors use [[Wikipedia]] articles to semantically inform the generation of query models. To this end, authors apply supervised machine learning to automatically link queries to Wikipedia articles and sample terms from the linked articles to re-estimate the query model. On a recent large web corpus, authors observe substantial gains in terms of both traditional metrics and diversity [[measures]]. | ||
Revision as of 11:18, 4 March 2021
Authors | Edgar Meij Maarten de Rijke |
|---|---|
Publication date | 2010 |
DOI | 10.1145/1835449.1835660 |
Links | Original |
Supervised Query Modeling Using Wikipedia - scientific work related to Wikipedia quality published in 2010, written by Edgar Meij and Maarten de Rijke.
Overview
Authors use Wikipedia articles to semantically inform the generation of query models. To this end, authors apply supervised machine learning to automatically link queries to Wikipedia articles and sample terms from the linked articles to re-estimate the query model. On a recent large web corpus, authors observe substantial gains in terms of both traditional metrics and diversity measures.
