EAGER: extending automatically gazetteers for entity recognition
O. Gunes, C. Schallhart, T. Furche, J. Lehmann, and A. Ngomo.
Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively Constructed Semantic Resources and their Applications to NLP, page 29--33. Association for Computational Linguistics, (July 2012)

Key to named entity recognition, the manual gazetteering of entity lists is a costly, errorprone process that often yields results that are incomplete and suffer from sampling bias. Exploiting current sources of structured information, we propose a novel method for extending minimal seed lists into complete gazetteers. Like previous approaches, we value W IKIPEDIA as a huge, well-curated, and relatively unbiased source of entities. However, in contrast to previous work, we exploit not only its content, but also its structure, as exposed in DBPEDIA. We extend gazetteers through Wikipedia categories, carefully limiting the impact of noisy categorizations. The resulting gazetteers easily outperform previous approaches on named entity recognition.

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@inproceedings{gunes2012eager,
  abstract = {Key to named entity recognition, the manual gazetteering of entity lists is a costly, errorprone process that often yields results that are incomplete and suffer from sampling bias. Exploiting current sources of structured information, we propose a novel method for extending minimal seed lists into complete gazetteers. Like previous approaches, we value W IKIPEDIA as a huge, well-curated, and relatively unbiased source of entities. However, in contrast to previous work, we exploit not only its content, but also its structure, as exposed in DBPEDIA. We extend gazetteers through Wikipedia categories, carefully limiting the impact of noisy categorizations. The resulting gazetteers easily outperform previous approaches on named entity recognition. },
  added-at = {2013-02-27T15:35:42.000+0100},
  author = {Gunes, Omer and Schallhart, Christian and Furche, Tim and Lehmann, Jens and Ngomo, Axel-Cyrille Ngonga},
  biburl = {https://puma.uni-kassel.de/bibtex/23eac4c009268cd4f2c264dd24053f8a6/jaeschke},
  booktitle = {Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively  Constructed Semantic Resources and their Applications to NLP},
  interhash = {20c47a41c89ff6c2a8f7bb524185b8ac},
  intrahash = {3eac4c009268cd4f2c264dd24053f8a6},
  keywords = {dbpedia eager entity extraction gazetteer ie named ner recognition wikipedia},
  month = jul,
  organization = {Association for Computational Linguistics},
  pages = {29--33},
  timestamp = {2013-03-05T17:30:29.000+0100},
  title = {EAGER: extending automatically gazetteers for entity recognition},
  url = {http://acl.eldoc.ub.rug.nl/mirror/W/W12/W12-4005.pdf},
  year = 2012
}

%0 Conference Paper
%1 gunes2012eager
%A Gunes, Omer
%A Schallhart, Christian
%A Furche, Tim
%A Lehmann, Jens
%A Ngomo, Axel-Cyrille Ngonga
%B Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively  Constructed Semantic Resources and their Applications to NLP
%D 2012
%K dbpedia eager entity extraction gazetteer ie named ner recognition wikipedia
%P 29--33
%T EAGER: extending automatically gazetteers for entity recognition
%U http://acl.eldoc.ub.rug.nl/mirror/W/W12/W12-4005.pdf
%X Key to named entity recognition, the manual gazetteering of entity lists is a costly, errorprone process that often yields results that are incomplete and suffer from sampling bias. Exploiting current sources of structured information, we propose a novel method for extending minimal seed lists into complete gazetteers. Like previous approaches, we value W IKIPEDIA as a huge, well-curated, and relatively unbiased source of entities. However, in contrast to previous work, we exploit not only its content, but also its structure, as exposed in DBPEDIA. We extend gazetteers through Wikipedia categories, carefully limiting the impact of noisy categorizations. The resulting gazetteers easily outperform previous approaches on named entity recognition.

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