@inproceedings{ashraf-etal-2024-bfci,
title = "{BFCI} at {A}ra{F}in{NLP}2024: Support Vector Machines for {A}rabic Financial Text Classification",
author = "Ashraf, Nsrin and
Nayel, Hamada and
Aldawsari, Mohammed and
Shashirekha, Hosahalli and
Elshishtawy, Tarek",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.42/",
doi = "10.18653/v1/2024.arabicnlp-1.42",
pages = "446--449",
abstract = "In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49{\%}. Our submission`s result is appropriate compared to the simplicity of the proposed model`s structure."
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<abstract>In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission‘s result is appropriate compared to the simplicity of the proposed model‘s structure.</abstract>
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%0 Conference Proceedings
%T BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification
%A Ashraf, Nsrin
%A Nayel, Hamada
%A Aldawsari, Mohammed
%A Shashirekha, Hosahalli
%A Elshishtawy, Tarek
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ashraf-etal-2024-bfci
%X In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission‘s result is appropriate compared to the simplicity of the proposed model‘s structure.
%R 10.18653/v1/2024.arabicnlp-1.42
%U https://aclanthology.org/2024.arabicnlp-1.42/
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.42
%P 446-449
Markdown (Informal)
[BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification](https://aclanthology.org/2024.arabicnlp-1.42/) (Ashraf et al., ArabicNLP 2024)
ACL