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    <abstract>This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F_1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public.</abstract>
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%0 Conference Proceedings
%T Arabic Named Entity Recognition: What Works and What’s Next
%A Liu, Liyuan
%A Shang, Jingbo
%A Han, Jiawei
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-arabic
%X This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F_1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public.
%R 10.18653/v1/W19-4607
%U https://aclanthology.org/W19-4607
%U https://doi.org/10.18653/v1/W19-4607
%P 60-67

Markdown (Informal)

[Arabic Named Entity Recognition: What Works and What’s Next](https://aclanthology.org/W19-4607) (Liu et al., WANLP 2019)

• Arabic Named Entity Recognition: What Works and What’s Next (Liu et al., WANLP 2019)

ACL

• Liyuan Liu, Jingbo Shang, and Jiawei Han. 2019. Arabic Named Entity Recognition: What Works and What’s Next. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 60–67, Florence, Italy. Association for Computational Linguistics.