FArSS: Fast and Efficient Semantic Question Similarity in Arabic
This paper addresses the challenge of efficient semantic question similarity in Arabic by leveraging fastText embeddings and a simple neural network architecture. Our model (FArSS) avoids the complexities of recurrent connections and attention mechanisms, resulting in a streamlined and efficient app...
Saved in:
Main Author: | Mohamed Alkaoud |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10840214/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers
by: Ehtesham Hashmi, et al.
Published: (2024-11-01) -
Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning
by: Ashagrew Liyih, et al.
Published: (2024-06-01) -
Application of Quantum Recurrent Neural Network in Low-Resource Language Text Classification
by: Wenbin Yu, et al.
Published: (2024-01-01) -
The Integration of AI and Metaverse in Education: A Systematic Literature Review
by: Khalid Almeman, et al.
Published: (2025-01-01) -
Implementation of Arabic Online Learning at Post-Pandemic of Covid 19
by: Aceng Rahmat, et al.
Published: (2024-01-01)