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Title: ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence
This study investigates the use of Natural Language Processing (NLP) methods to analyze politics, conflicts and violence in the Middle East using domain-specific pre-trained language models. We introduce Arabic text and present ConfliBERT-Arabic, a pre-trained language models that can efficiently analyze political, conflict and violence-related texts. Our technique hones a pre-trained model using a corpus of Arabic texts about regional politics and conflicts. Performance of our models is compared to baseline BERT models. Our findings show that the performance of NLP models for Middle Eastern politics and conflict analysis are enhanced by the use of domain-specific pre-trained local language models. This study offers political and conflict analysts, including policymakers, scholars, and practitioners new approaches and tools for deciphering the intricate dynamics of local politics and conflicts directly in Arabic.  more » « less
Award ID(s):
2311142
PAR ID:
10522657
Author(s) / Creator(s):
; ; ; ; ; ; ;
Editor(s):
Mitkov, Ruslan; Angelova, Galia
Publisher / Repository:
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing https://aclanthology.org/2023.ranlp-1.11
Date Published:
Page Range / eLocation ID:
98--108
Subject(s) / Keyword(s):
https://aclanthology.org/2023.ranlp-1.11
Format(s):
Medium: X
Location:
Varna, Bulgaria
Sponsoring Org:
National Science Foundation
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