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        <namePart type="given">Axel-Cyrille</namePart>
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        <namePart type="given">René</namePart>
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            <namePart type="given">Martijn</namePart>
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    <abstract>The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present Bengal, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by Bengal and on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at http://faturl.com/bengalexpinlg and the code at https://github.com/dice-group/BENGAL.</abstract>
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%0 Conference Proceedings
%T BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking
%A Ngonga Ngomo, Axel-Cyrille
%A Röder, Michael
%A Moussallem, Diego
%A Usbeck, Ricardo
%A Speck, René
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F ngonga-ngomo-etal-2018-bengal
%X The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present Bengal, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by Bengal and on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at http://faturl.com/bengalexpinlg and the code at https://github.com/dice-group/BENGAL.
%R 10.18653/v1/W18-6541
%U https://aclanthology.org/W18-6541
%U https://doi.org/10.18653/v1/W18-6541
%P 339-349

Markdown (Informal)

[BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking](https://aclanthology.org/W18-6541) (Ngonga Ngomo et al., INLG 2018)

• BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking (Ngonga Ngomo et al., INLG 2018)

ACL

• Axel-Cyrille Ngonga Ngomo, Michael Röder, Diego Moussallem, Ricardo Usbeck, and René Speck. 2018. BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking. In Proceedings of the 11th International Conference on Natural Language Generation, pages 339–349, Tilburg University, The Netherlands. Association for Computational Linguistics.