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        <title>Simple Effective Microblog Named Entity Recognition: Arabic as an Example</title>
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    <abstract>Despite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as informality of language, shortened named entities, brevity of expressions, and inconsistent capitalization (for cased languages). We introduce simple effective language-independent approaches for improving NER on microblogs, based on using large gazetteers, domain adaptation, and a two-pass semi-supervised method. We use Arabic as an example language to compare the relative effectiveness of the approaches and when best to use them. We also present a new dataset for the task. Results of combining the proposed approaches show an improvement of 35.3 F-measure points over a baseline system trained on news data and an improvement of 19.9 F-measure points over the same system but trained on microblog data.</abstract>
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%0 Conference Proceedings
%T Simple Effective Microblog Named Entity Recognition: Arabic as an Example
%A Darwish, Kareem
%A Gao, Wei
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F darwish-gao-2014-simple
%X Despite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as informality of language, shortened named entities, brevity of expressions, and inconsistent capitalization (for cased languages). We introduce simple effective language-independent approaches for improving NER on microblogs, based on using large gazetteers, domain adaptation, and a two-pass semi-supervised method. We use Arabic as an example language to compare the relative effectiveness of the approaches and when best to use them. We also present a new dataset for the task. Results of combining the proposed approaches show an improvement of 35.3 F-measure points over a baseline system trained on news data and an improvement of 19.9 F-measure points over the same system but trained on microblog data.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/186_Paper.pdf
%P 2513-2517

Markdown (Informal)

[Simple Effective Microblog Named Entity Recognition: Arabic as an Example](http://www.lrec-conf.org/proceedings/lrec2014/pdf/186_Paper.pdf) (Darwish & Gao, LREC 2014)

• Simple Effective Microblog Named Entity Recognition: Arabic as an Example (Darwish & Gao, LREC 2014)

ACL

• Kareem Darwish and Wei Gao. 2014. Simple Effective Microblog Named Entity Recognition: Arabic as an Example. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2513–2517, Reykjavik, Iceland. European Language Resources Association (ELRA).