Work

IDRISI-D: Arabic and English Datasets and Benchmarks for Location Mention Disambiguation over Disaster Microblogs

Year: 2023

Type: conference-paper

Abstract: Extracting and disambiguating geolocation information from social media data enables effective disaster management, as it helps response authorities; for example, locating incidents for planning rescue activities and affected people for evacuation.Nevertheless, the dearth of resources and tools hinders the development and evaluation of Location Mention Disambiguation (LMD) models in the disaster management domain.Consequently, the LMD task is greatly understudied, especially for the low resource languages such as Arabic.To fill this gap, we introduce IDRISI-D, the largest to date English and the first Arabic public LMD datasets.Additionally, we introduce a modified hierarchical evaluation framework that offers a lenient and nuanced evaluation of LMD systems.We further benchmark IDRISI-D datasets using representative baselines and show the competitiveness of BERT-based models.

Source: Arabic Natural Language Processing Conference (ArabicNLP)

DOI: 10.18653/v1/2023.arabicnlp-1.14

Authors: Reem Suwaileh, Tamer Elsayed, Muhammad Imran

Institutions: Qatar University, Hamad bin Khalifa University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Qatar Foundation, Fonds National de la Recherche Luxembourg, Qatar National Research Fund

Award: G607944291