Difference between revisions of "Improved Text Categorisation for Wikipedia Named Entities"
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| + | | title = Improved Text Categorisation for Wikipedia Named Entities | ||
| + | | date = 2009 | ||
| + | | authors = [[Sam Tardif]]<br />[[James R. Curran]]<br />[[Tara Murphy]] | ||
| + | | link = http://www.aclweb.org/anthology/U09-1015 | ||
| + | }} | ||
'''Improved Text Categorisation for Wikipedia Named Entities''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sam Tardif]], [[James R. Curran]] and [[Tara Murphy]]. | '''Improved Text Categorisation for Wikipedia Named Entities''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Sam Tardif]], [[James R. Curran]] and [[Tara Murphy]]. | ||
== Overview == | == Overview == | ||
The accuracy of [[named entity recognition]] systems relies heavily upon the volume and quality of available training data. Improving the process of automatically producing such training data is an important task, as manual acquisition is both time consuming and expensive. Authors explore the use of a variety of machine learning algorithms for categorising [[Wikipedia]] articles, an initial step in producing the [[named entity]] training data. Authors were able to achieve a categorisation accuracy of 95% F -score over six coarse [[categories]], an improvement of up to 5% F -score over previous methods. | The accuracy of [[named entity recognition]] systems relies heavily upon the volume and quality of available training data. Improving the process of automatically producing such training data is an important task, as manual acquisition is both time consuming and expensive. Authors explore the use of a variety of machine learning algorithms for categorising [[Wikipedia]] articles, an initial step in producing the [[named entity]] training data. Authors were able to achieve a categorisation accuracy of 95% F -score over six coarse [[categories]], an improvement of up to 5% F -score over previous methods. | ||
Revision as of 23:57, 24 October 2019
Authors | Sam Tardif James R. Curran Tara Murphy |
|---|---|
Publication date | 2009 |
Links | Original |
Improved Text Categorisation for Wikipedia Named Entities - scientific work related to Wikipedia quality published in 2009, written by Sam Tardif, James R. Curran and Tara Murphy.
Overview
The accuracy of named entity recognition systems relies heavily upon the volume and quality of available training data. Improving the process of automatically producing such training data is an important task, as manual acquisition is both time consuming and expensive. Authors explore the use of a variety of machine learning algorithms for categorising Wikipedia articles, an initial step in producing the named entity training data. Authors were able to achieve a categorisation accuracy of 95% F -score over six coarse categories, an improvement of up to 5% F -score over previous methods.
