Arabic Named Entity Recognition: What Works and What’s Next

Liyuan Liu, Jingbo Shang, Jiawei Han


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.
Anthology ID:
W19-4607
Volume:
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WANLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–67
URL:
https://www.aclweb.org/anthology/W19-4607
DOI:
10.18653/v1/W19-4607
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PDF:
https://www.aclweb.org/anthology/W19-4607.pdf