Difference between revisions of "Automatically Developing a Fine-Grained Arabic Named Entity Corpus and Gazetteer by Utilizing Wikipedia"
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| + | | title = Automatically Developing a Fine-Grained Arabic Named Entity Corpus and Gazetteer by Utilizing Wikipedia | ||
| + | | date = 2013 | ||
| + | | authors = [[Fahd Alotaibi]]<br />[[Mark G. Lee]] | ||
| + | | link = http://www.aclweb.org/anthology/I13-1045 | ||
| + | }} | ||
'''Automatically Developing a Fine-Grained Arabic Named Entity Corpus and Gazetteer by Utilizing Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Fahd Alotaibi]] and [[Mark G. Lee]]. | '''Automatically Developing a Fine-Grained Arabic Named Entity Corpus and Gazetteer by Utilizing Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Fahd Alotaibi]] and [[Mark G. Lee]]. | ||
== Overview == | == Overview == | ||
This paper presents a methodology to exploit the potential of [[Arabic Wikipedia]] to assist in the automatic development of a large Fine-grained Named Entity (NE) corpus and gazetteer. The corner stone of this approach is efficient classification of [[Wikipedia]] articles to target NE classes. The resources developed were thoroughly evaluated to ensure [[reliability]] and a high quality. Results show the developed gazetteer boosts the performance of the NE classifier on a news-wire domain by at least 2 points F-measure. Moreover, by combining a learning NE classifier with the developed corpus the score achieved is a high F-measure of 85.18%. The developed resources overcome the limitations of traditional Arabic NE tasks by more fine-grained analysis and providing a beneficial route for further studies. | This paper presents a methodology to exploit the potential of [[Arabic Wikipedia]] to assist in the automatic development of a large Fine-grained Named Entity (NE) corpus and gazetteer. The corner stone of this approach is efficient classification of [[Wikipedia]] articles to target NE classes. The resources developed were thoroughly evaluated to ensure [[reliability]] and a high quality. Results show the developed gazetteer boosts the performance of the NE classifier on a news-wire domain by at least 2 points F-measure. Moreover, by combining a learning NE classifier with the developed corpus the score achieved is a high F-measure of 85.18%. The developed resources overcome the limitations of traditional Arabic NE tasks by more fine-grained analysis and providing a beneficial route for further studies. | ||
Revision as of 14:34, 27 June 2020
Authors | Fahd Alotaibi Mark G. Lee |
|---|---|
Publication date | 2013 |
Links | Original |
Automatically Developing a Fine-Grained Arabic Named Entity Corpus and Gazetteer by Utilizing Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Fahd Alotaibi and Mark G. Lee.
Overview
This paper presents a methodology to exploit the potential of Arabic Wikipedia to assist in the automatic development of a large Fine-grained Named Entity (NE) corpus and gazetteer. The corner stone of this approach is efficient classification of Wikipedia articles to target NE classes. The resources developed were thoroughly evaluated to ensure reliability and a high quality. Results show the developed gazetteer boosts the performance of the NE classifier on a news-wire domain by at least 2 points F-measure. Moreover, by combining a learning NE classifier with the developed corpus the score achieved is a high F-measure of 85.18%. The developed resources overcome the limitations of traditional Arabic NE tasks by more fine-grained analysis and providing a beneficial route for further studies.
