Datasets:

drelhaj
/

Arabic-Dialects

license: cc-by-4.0
language:
  - ar
task_categories:
  - text-classification
tags:
  - arabic
  - dialect-identification
  - multilingual
  - sociolinguistics
  - code-switching
  - bivalency
configs:
  - config_name: full_text
    description: Raw dialectal text files for EGY, GLF, LAV, NOR and MSA.
  - config_name: freq_lists
    description: >-
      Dialect-specific vocabulary lists, bivalency-removed lists and MSA shared
      lists.

Arabic Dialects Dataset (Bivalency & Code-Switching)

The Arabic Dialects Dataset is a specialised corpus designed for automatic dialect identification, with a focus on the linguistic phenomena of bivalency and written code-switching between major Arabic dialects and Modern Standard Arabic (MSA).
It covers five varieties:

EGY – Egyptian Arabic
GLF – Gulf Arabic
LAV – Levantine Arabic
NOR – North African / Tunisian Arabic
MSA – Modern Standard Arabic

The dataset was created for research on fine-grained linguistic variation and has been used to evaluate new methods such as Subtractive Bivalency Profiling (SBP), achieving over 76% accuracy in supervised dialect identification.

This HuggingFace release makes the dataset machine-readable and ready for text classification, feature engineering, or corpus linguistic exploration.

📘 Citation

If you use this dataset, please cite:

El-Haj M., Rayson P., Aboelezz M. (2018)
Arabic Dialect Identification in the Context of Bivalency and Code-Switching.
In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, pp. 3622–3627.
European Language Resources Association (ELRA).
PDF: https://elhaj.uk/docs/237_Paper.pdf

📂 Dataset Structure

The dataset is distributed across two main sections:

1. Dialects Full Text

Five files, each containing all instances belonging to one dialect:

Dialects Full Text/
│── allEGY.txt
│── allGLF.txt
│── allLAV.txt
│── allMSA.txt
└── allNOR.txt

Each file contains raw text samples, one per line, suitable for direct use in dialect classification experiments.

2. Dialectal Frequency Lists

These resources support linguistic analysis and the SBP approach introduced in the paper.

Dialects Frequency Lists/
│
├── Bivalency Removed (dialect - MSA)/
│ allEGY_minusMSA.txt
│ allGLF_minusMSA.txt
│ allLAV_minusMSA.txt
│ allNOR_minusMSA.txt
│
├── Dialects’ MSA/
│ allEGY_dialectal_MSA.txt
│ allGLF_dialectal_MSA.txt
│ allLAV_dialectal_MSA.txt
│ allNOR_dialectal_MSA.txt
│
├── Dialects Tokens WITH Frequency Count/
│ all-EGY_FreqList.txt
│ all-GLF_FreqList.txt
│ all-LAV_FreqList.txt
│ all-MSA_FreqList.txt
│ all-NOR_FreqList.txt
│
└── Dialects Tokens NO Frequency Count/
all-EGY_FreqList2.txt
all-GLF_FreqList2.txt
all-LAV_FreqList2.txt
all-MSA_FreqList2.txt
all-NOR_FreqList2.txt

These include:

Bivalency-removed lists: dialect-specific vocabularies after removing shared (bivalent) words
Dialectal-MSA lists: vocabulary shared with MSA via written code-switching
Frequency lists: token frequency counts
Non-frequency lists: raw token lists without counts

📊 CSV Conversion + Train/Dev/Test Splits

The original text files were converted into a unified sentence-level CSV: arabic_dialects_full.csv

with the schema:

sentence	dialect
…	EGY / GLF / LAV / MSA / NOR

Each sentence corresponds to one line from the original files.

This CSV was then stratified and split into: arabic_dialects_train.csv arabic_dialects_dev.csv arabic_dialects_test.csv

Splits preserve dialect balance and follow an 80/10/10 ratio.

These files power the csv_splits configuration for immediate model training.

🧪 Intended Use

This dataset supports several research tasks:

Dialect Identification

Train machine-learning models to classify EGY/GLF/LAV/NOR/MSA
Evaluate performance on highly bivalent and lexically overlapping dialects
Benchmark features beyond n-grams

Bivalency & Code-Switching Analysis

Study how words shift between dialects and MSA
Explore written code-switching in online text and commentary discourse

Linguistic Feature Engineering

Use SBP lists as interpretable features
Combine stylistic, grammatical, and frequency-based signals

📊 Data Statistics

(From the original publication)

Dialect	Sentences	Words
EGY	4,061	118,152
GLF	2,546	65,752
LAV	2,463	67,976
MSA	3,731	49,985
NOR	3,693	53,204
Total	16,494	355,069

🔍 Example Usage

Load the full text for dialect classification

from datasets import load_dataset

ds = load_dataset("YOUR_REPO_NAME", "full_text")
print(ds["train"][0])
ds = load_dataset("YOUR_REPO_NAME", "freq_lists")
freq_list = ds["EGY_freq"]

⚠ Licence

This dataset is released for research purposes only. Texts originate from publicly available online sources or earlier datasets where redistribution for research is permitted.

🙏 Acknowledgements

This dataset was developed at UCREL, Lancaster University, as part of research into Arabic dialect variation, bivalency, and automatic dialect identification.