Year: 2023
Type: conference-paper
Abstract: Extracting geolocation information from social media data enables effective disaster management, as it helps response authorities; for example, in locating incidents for planning rescue activities, and affected people for evacuation.Nevertheless, geolocation extraction is greatly understudied for the low resource languages such as Arabic.To fill this gap, we introduce IDRISI-RA, the first publicly-available Arabic Location Mention Recognition (LMR) dataset that provides human-and automatically-labeled versions in order of thousands and millions of tweets, respectively.It contains both location mentions and their types (e.g., district, city).Our extensive analysis shows the decent geographical, domain, location granularity, temporal, and dialectical coverage of IDRISI-RA.Furthermore, we establish baselines using the standard Arabic NER models and build two simple, yet effective, LMR models.Our rigorous experiments confirm the need for developing specific models for Arabic LMR in the disaster domain.Moreover, experiments show the promising domain and geographical generalizability of IDRISI-RA under zero-shot learning.
Authors: Reem Suwaileh, Muhammad Imran, Tamer Elsayed
Institutions: Hamad bin Khalifa University, Qatar University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G607944291
OpenAlex ID: W4385572050