Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm
Hadeer Adel,
Abdelghani Dahou,
Alhassan Mabrouk,
Mohamed Abd Elaziz,
Mohammed Kayed,
Ibrahim Mahmoud El-Henawy,
Samah Alshathri,
Abdelmgeid Amin Ali
Affiliations
Hadeer Adel
Department of Computer Science, Faculty of Computer Science, Nahda University, Beni Suef 62511, Egypt
Abdelghani Dahou
Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria
Alhassan Mabrouk
Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt
Mohamed Abd Elaziz
Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
Mohammed Kayed
Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt
Ibrahim Mahmoud El-Henawy
Department of Computer Science, Faculty of Computer Science, Zagazig University, Zagazig 44519, Egypt
Samah Alshathri
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelmgeid Amin Ali
Faculty of Computer Science and Information, Minia University, Minia 61519, Egypt
This paper presents an alternative event detection model based on the integration between the DistilBERT and a new meta-heuristic technique named the Hunger Games Search (HGS). The DistilBERT aims to extract features from the text dataset, while a binary version of HGS is developed as a feature selection (FS) approach, which aims to remove the irrelevant features from those extracted. To assess the developed model, a set of experiments are conducted using a set of real-world datasets. In addition, we compared the binary HGS with a set of well-known FS algorithms, as well as the state-of-the-art event detection models. The comparison results show that the proposed model is superior to other methods in terms of performance measures.
