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        <title>Combining Minimally-supervised Methods for Arabic Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Maha</namePart>
        <namePart type="family">Althobaiti</namePart>
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    <name type="personal">
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    <abstract>Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00136</identifier>
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        <date>2015</date>
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%0 Journal Article
%T Combining Minimally-supervised Methods for Arabic Named Entity Recognition
%A Althobaiti, Maha
%A Kruschwitz, Udo
%A Poesio, Massimo
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F althobaiti-etal-2015-combining
%X Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.
%R 10.1162/tacl_a_00136
%U https://aclanthology.org/Q15-1018
%U https://doi.org/10.1162/tacl_a_00136
%P 243-255

Markdown (Informal)

[Combining Minimally-supervised Methods for Arabic Named Entity Recognition](https://aclanthology.org/Q15-1018) (Althobaiti et al., TACL 2015)

• Combining Minimally-supervised Methods for Arabic Named Entity Recognition (Althobaiti et al., TACL 2015)

ACL

• Maha Althobaiti, Udo Kruschwitz, and Massimo Poesio. 2015. Combining Minimally-supervised Methods for Arabic Named Entity Recognition. Transactions of the Association for Computational Linguistics, 3:243–255.