Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm
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 sele...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/3/447 |
| _version_ | 1797486375243612160 |
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| author | Hadeer Adel Abdelghani Dahou Alhassan Mabrouk Mohamed Abd Elaziz Mohammed Kayed Ibrahim Mahmoud El-Henawy Samah Alshathri Abdelmgeid Amin Ali |
| author_facet | Hadeer Adel Abdelghani Dahou Alhassan Mabrouk Mohamed Abd Elaziz Mohammed Kayed Ibrahim Mahmoud El-Henawy Samah Alshathri Abdelmgeid Amin Ali |
| author_sort | Hadeer Adel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:32:16Z |
| format | Article |
| id | doaj.art-8b839f31a86348caa820ba9dede9bc91 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:32:16Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8b839f31a86348caa820ba9dede9bc912023-11-23T17:07:40ZengMDPI AGMathematics2227-73902022-01-0110344710.3390/math10030447Improving Crisis Events Detection Using DistilBERT with Hunger Games Search AlgorithmHadeer Adel0Abdelghani Dahou1Alhassan Mabrouk2Mohamed Abd Elaziz3Mohammed Kayed4Ibrahim Mahmoud El-Henawy5Samah Alshathri6Abdelmgeid Amin Ali7Department of Computer Science, Faculty of Computer Science, Nahda University, Beni Suef 62511, EgyptMathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, AlgeriaMathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, EgyptFaculty of Computer Science and Engineering, Galala University, Suez 435611, EgyptComputer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, EgyptDepartment of Computer Science, Faculty of Computer Science, Zagazig University, Zagazig 44519, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Computer Science and Information, Minia University, Minia 61519, EgyptThis 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.https://www.mdpi.com/2227-7390/10/3/447event detectiondeep learninghunger game searchDistilBERTfeature selection optimization algorithms |
| spellingShingle | Hadeer Adel Abdelghani Dahou Alhassan Mabrouk Mohamed Abd Elaziz Mohammed Kayed Ibrahim Mahmoud El-Henawy Samah Alshathri Abdelmgeid Amin Ali Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm Mathematics event detection deep learning hunger game search DistilBERT feature selection optimization algorithms |
| title | Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm |
| title_full | Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm |
| title_fullStr | Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm |
| title_full_unstemmed | Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm |
| title_short | Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm |
| title_sort | improving crisis events detection using distilbert with hunger games search algorithm |
| topic | event detection deep learning hunger game search DistilBERT feature selection optimization algorithms |
| url | https://www.mdpi.com/2227-7390/10/3/447 |
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