# USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification

arXiv: https://arxiv.org/abs/2312.10536 · HTML: https://mlsanity.com/paper/2312.10536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2010.11334/) (2020)
- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)
- [NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2310.16117/) (2023)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Combating Disinformation in a Social Media Age](https://mlsanity.com/paper/2007.07388/) (2020)
- [Machine Generation and Detection of Arabic Manipulated and Fake News](https://mlsanity.com/paper/2011.03092/) (2020)

## Built on by (most cited first, 3 of 3)

- [dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents](https://mlsanity.com/paper/2407.13565/) (2024)
- [dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers](https://mlsanity.com/paper/2407.13603/) (2024)
- [dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features](https://mlsanity.com/paper/2407.13608/) (2024)

## Abstract (arXiv)

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI'2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. For classification purposes, we employ the Linear Support Vector Classification (LSVC) model. During the evaluation phase, our system demonstrates noteworthy results, achieving an F1 score of 62.51%. This achievement closely aligns with the average F1 scores attained by other systems submitted for the first subtask, which stands at 72.91%.