@inproceedings{35f52e9eddee4b13bfb7c2da06426f8e,
title = "ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence",
abstract = "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.",
author = "Sultan Alsarra and Luay Abdeljaber and Wooseong Yang and Niamat Zawad and Latifur Khan and Brandt, {Patrick T.} and Javier Osorio and D'Orazio, {Vito J.}",
note = "Publisher Copyright: {\textcopyright} 2023 Incoma Ltd. All rights reserved.; 2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023 ; Conference date: 04-09-2023 Through 06-09-2023",
year = "2023",
doi = "10.26615/978-954-452-092-2_011",
language = "English (US)",
series = "International Conference Recent Advances in Natural Language Processing, RANLP",
publisher = "Incoma Ltd",
pages = "98--108",
editor = "Galia Angelova and Maria Kunilovskaya and Ruslan Mitkov",
booktitle = "International Conference Recent Advances in Natural Language Processing, RANLP 2023",
}