Difference between revisions of "Analysing Wikipedia and Gold-Standard Corpora for Ner Training"
(+ wikilinks) |
(Infobox work) |
||
| Line 1: | Line 1: | ||
| + | {{Infobox work | ||
| + | | title = Analysing Wikipedia and Gold-Standard Corpora for Ner Training | ||
| + | | date = 2009 | ||
| + | | authors = [[Joel Nothman]]<br />[[Tara Murphy]]<br />[[James R. Curran]] | ||
| + | | doi = 10.3115/1609067.1609135 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=1609067.1609135 | ||
| + | }} | ||
'''Analysing Wikipedia and Gold-Standard Corpora for Ner Training''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Joel Nothman]], [[Tara Murphy]] and [[James R. Curran]]. | '''Analysing Wikipedia and Gold-Standard Corpora for Ner Training''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Joel Nothman]], [[Tara Murphy]] and [[James R. Curran]]. | ||
== Overview == | == Overview == | ||
Named [[entity recognition]] (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used [[Wikipedia]] to automatically create a massive corpus of [[named entity]] annotated text. | Named [[entity recognition]] (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used [[Wikipedia]] to automatically create a massive corpus of [[named entity]] annotated text. | ||
Revision as of 11:29, 1 December 2019
Authors | Joel Nothman Tara Murphy James R. Curran |
|---|---|
Publication date | 2009 |
DOI | 10.3115/1609067.1609135 |
Links | Original |
Analysing Wikipedia and Gold-Standard Corpora for Ner Training - scientific work related to Wikipedia quality published in 2009, written by Joel Nothman, Tara Murphy and James R. Curran.
Overview
Named entity recognition (ner) for English typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. Recent work has used Wikipedia to automatically create a massive corpus of named entity annotated text.
