Work

ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

Year: 2023

Type: conference-paper

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.

Source: International conference Recent advances in natural language processing

DOI: 10.26615/978-954-452-092-2_011

Authors: Sultan Alsarra, Luay Abdeljaber, Wooseong Yang, Niamat Zawad, Latifur Khan, Patrick T. Brandt, Javier Osorio, Vito J. D’Orazio

Institutions: The University of Texas at Dallas, University of Arizona, West Virginia University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Funders: National Science Foundation, University of Arizona, University of Illinois at Urbana-Champaign, Office of Naval Research, Office of Advanced Cyberinfrastructure, Army Research Office

Awards: Elements: Data: Sustaining Modern Infrastructure For Political And Social Event Data, G1453181110, G1758511527, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, Collaborative Research: EAGER: SaTC-EDU: Secure and Privacy-Preserving Adaptive Artificial Intelligence Curriculum Development for Cybersecurity, MRI: Development of An Instrument for Secure Cyber Physical Systems Analytics, G3562386289, G3997935774, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8876996369, G899398412, G942180547

OpenAlex ID: W4388666050

FWCI: 1.237

Cites: 39

Cited by: 7

Related to: 10