Abstract
The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.
Original language | English |
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Title of host publication | WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop |
Editors | Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 115-125 |
Number of pages | 11 |
ISBN (Electronic) | 9781954085091 |
State | Published - 2021 |
Event | 6th Arabic Natural Language Processing Workshop, WANLP 2021 - Virtual, Kyiv, Ukraine Duration: 19 Apr 2021 → … |
Publication series
Name | WANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop |
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Conference
Conference | 6th Arabic Natural Language Processing Workshop, WANLP 2021 |
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Country/Territory | Ukraine |
City | Virtual, Kyiv |
Period | 19/04/21 → … |
Bibliographical note
Publisher Copyright:© WANLP 2021 - 6th Arabic Natural Language Processing Workshop
ASJC Scopus subject areas
- Language and Linguistics
- Computational Theory and Mathematics
- Software
- Linguistics and Language