Difference between revisions of "A Comparable Wikipedia Corpus: from Wiki Syntax to Pos Tagged Xml"
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| − | '''A Comparable Wikipedia Corpus: from Wiki Syntax to Pos Tagged Xml''' - scientific work related to Wikipedia quality published in 2011, written by Noah Bubenhofer, Stefanie Haupt and Horst Schwinn. | + | '''A Comparable Wikipedia Corpus: from Wiki Syntax to Pos Tagged Xml''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Noah Bubenhofer]], [[Stefanie Haupt]] and [[Horst Schwinn]]. |
== Overview == | == Overview == | ||
| − | To build a comparable Wikipedia corpus of German, French, Italian, Norwegian, Polish and Hungarian for contrastive grammar research, authors used a set of XSLT stylesheets to transform the mediawiki anntations to XML. Furthermore, the data has been amnntated with word class information using different taggers. The outcome is a corpus with rich meta data and linguistic annotation that can be used for multilingual research in various linguistic topics. | + | To build a comparable [[Wikipedia]] corpus of German, French, Italian, Norwegian, Polish and Hungarian for contrastive grammar research, authors used a set of XSLT stylesheets to transform the mediawiki anntations to XML. Furthermore, the data has been amnntated with word class information using different taggers. The outcome is a corpus with rich meta data and linguistic annotation that can be used for [[multilingual]] research in various linguistic topics. |
Revision as of 10:56, 8 September 2019
A Comparable Wikipedia Corpus: from Wiki Syntax to Pos Tagged Xml - scientific work related to Wikipedia quality published in 2011, written by Noah Bubenhofer, Stefanie Haupt and Horst Schwinn.
Overview
To build a comparable Wikipedia corpus of German, French, Italian, Norwegian, Polish and Hungarian for contrastive grammar research, authors used a set of XSLT stylesheets to transform the mediawiki anntations to XML. Furthermore, the data has been amnntated with word class information using different taggers. The outcome is a corpus with rich meta data and linguistic annotation that can be used for multilingual research in various linguistic topics.
