Difference between revisions of "A Novel Approach to Automatic Gazetteer Generation Using Wikipedia"
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| + | {{Infobox work | ||
| + | | title = A Novel Approach to Automatic Gazetteer Generation Using Wikipedia | ||
| + | | date = 2009 | ||
| + | | authors = [[Ziqi Zhang]]<br />[[José Iria]] | ||
| + | | doi = 10.3115/1699765.1699766 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=1699765.1699766 | ||
| + | }} | ||
'''A Novel Approach to Automatic Gazetteer Generation Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Ziqi Zhang]] and [[José Iria]]. | '''A Novel Approach to Automatic Gazetteer Generation Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2009, written by [[Ziqi Zhang]] and [[José Iria]]. | ||
== Overview == | == Overview == | ||
Gazetteers or entity dictionaries are important knowledge resources for solving a wide range of NLP problems, such as entity extraction. Authors introduce a novel method to automatically generate gazetteers from seed lists using an external knowledge resource, the [[Wikipedia]]. Unlike previous methods, method exploits the rich content and various structural elements of Wikipedia, and does not rely on language- or domain-specific knowledge. Furthermore, applying the extended gazetteers to an entity extraction task in a scientific domain, authors empirically observed a significant improvement in system accuracy when compared with those using seed gazetteers. | Gazetteers or entity dictionaries are important knowledge resources for solving a wide range of NLP problems, such as entity extraction. Authors introduce a novel method to automatically generate gazetteers from seed lists using an external knowledge resource, the [[Wikipedia]]. Unlike previous methods, method exploits the rich content and various structural elements of Wikipedia, and does not rely on language- or domain-specific knowledge. Furthermore, applying the extended gazetteers to an entity extraction task in a scientific domain, authors empirically observed a significant improvement in system accuracy when compared with those using seed gazetteers. | ||
Revision as of 09:02, 3 October 2020
Authors | Ziqi Zhang José Iria |
|---|---|
Publication date | 2009 |
DOI | 10.3115/1699765.1699766 |
Links | Original |
A Novel Approach to Automatic Gazetteer Generation Using Wikipedia - scientific work related to Wikipedia quality published in 2009, written by Ziqi Zhang and José Iria.
Overview
Gazetteers or entity dictionaries are important knowledge resources for solving a wide range of NLP problems, such as entity extraction. Authors introduce a novel method to automatically generate gazetteers from seed lists using an external knowledge resource, the Wikipedia. Unlike previous methods, method exploits the rich content and various structural elements of Wikipedia, and does not rely on language- or domain-specific knowledge. Furthermore, applying the extended gazetteers to an entity extraction task in a scientific domain, authors empirically observed a significant improvement in system accuracy when compared with those using seed gazetteers.
