Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
Arabic
Size:
10K - 100K
Tags:
readability
License:
File size: 6,190 Bytes
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---
language:
- ar
license: cc-by-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-classification
pretty_name: BAREC Corpus v1.0
tags:
- readability
dataset_info:
features:
- name: ID
dtype: int64
- name: Sentence
dtype: string
- name: Word_Count
dtype: int64
- name: Word
dtype: string
- name: Lex
dtype: string
- name: D3Tok
dtype: string
- name: D3Lex
dtype: string
- name: Readability_Level
dtype: string
- name: Readability_Level_19
dtype: int64
- name: Readability_Level_7
dtype: int64
- name: Readability_Level_5
dtype: int64
- name: Readability_Level_3
dtype: int64
- name: Annotator
dtype: string
- name: Document
dtype: string
- name: Source
dtype: string
- name: Book
dtype: string
- name: Author
dtype: string
- name: Domain
dtype: string
- name: Text_Class
dtype: string
splits:
- name: train
num_bytes: 46655304
num_examples: 54845
- name: dev
num_bytes: 5917980
num_examples: 7310
- name: test
num_bytes: 6139540
num_examples: 7286
download_size: 23742307
dataset_size: 58712824
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# BAREC Corpus v1.0
## Dataset Summary
**BAREC** (the Balanced Arabic Readability Evaluation Corpus) is a large-scale dataset for **fine-grained Arabic readability assessment**.
The dataset includes over **1M words**, annotated at the **sentence level** across **19 readability levels**, with additional mappings to coarser 7, 5, and 3 level schemes.
---
## Supported Tasks
The dataset supports **multi-class readability classification** in the following formats:
- **19 levels** (default)
- **7 levels**
- **5 levels**
- **3 levels**
---
### Languages
- **Arabic** (Modern Standard Arabic)
---
## Dataset Structure
### Data Instances
`{'ID': 10100010008, 'Sentence': 'عيد سعيد', 'Word_Count': 2, 'Word': 'عيد سعيد', 'Lex': 'عيد سعيد', 'D3Tok': 'عيد سعيد', 'D3Lex': 'عيد سعيد', 'Readability_Level': '2-ba', 'Readability_Level_19': 2, 'Readability_Level_7': 1, 'Readability_Level_5': 1, 'Readability_Level_3': 1, 'Annotator': 'A4', 'Document': 'BAREC_Majed_0229_1983_001.txt', 'Source': 'Majed', 'Book': 'Edition: 229', 'Author': '#', 'Domain': 'Arts & Humanities', 'Text_Class': 'Foundational'}`
### Data Fields
- **ID**: Unique sentence identifier.
- **Sentence**: The sentence text.
- **Word_Count**: Number of words in the sentence.
- **Word**: Simply tokenized and dediacritized sentences.
- **Lex**: Each word is replaced by its predicited lemma (dediacritized).
- **D3Tok**: We tokenize words into their base and clitics forms.
- **D3Lex**: We replace the base forms in **D3Tok** with the predicited lemmas.
- **Readability_Level**: The readability level in `19-levels` scheme, ranging from `1-alif` to `19-qaf`.
- **Readability_Level_19**: The readability level in `19-levels` scheme, ranging from `1` to `19`.
- **Readability_Level_7**: The readability level in `7-levels` scheme, ranging from `1` to `7`.
- **Readability_Level_5**: The readability level in `5-levels` scheme, ranging from `1` to `5`.
- **Readability_Level_3**: The readability level in `3-levels` scheme, ranging from `1` to `3`.
- **Annotator**: The annotator ID (`A1-A5` or `IAA`).
- **Document**: Source document file name.
- **Source**: Document source.
- **Book**: Book name.
- **Author**: Author name.
- **Domain**: Domain (`Arts & Humanities`, `STEM` or `Social Sciences`).
- **Text_Class**: Readership group (`Foundational`, `Advanced` or `Specialized`).
### Data Splits
- The BAREC dataset has three splits: *Train* (80%), *Dev* (10%), and *Test* (10%).
- The splits are in the document level.
- The splits are balanced accross *Readability Levels*, *Domains*, and *Text Classes*.
---
## Evaluation
We define the Readability Assessment task as an ordinal classification task. The following metrics are used for evaluation:
- **Accuracy (Acc<sup>19</sup>):** The percentage of cases where reference and prediction classes match in the 19-level scheme.
- **Accuracy (Acc<sup>7</sup>, Acc<sup>5</sup>, Acc<sup>3</sup>):** The percentage of cases where reference and prediction classes match after collapsing the 19 levels into 7, 5, or 3 levels, respectively.
- **Adjacent Accuracy (±1 Acc<sup>19</sup>):** Also known as off-by-1 accuracy. The proportion of predictions that are either exactly correct or off by at most one level in the 19-level scheme.
- **Average Distance (Dist):** Also known as Mean Absolute Error (MAE). Measures the average absolute difference between predicted and true labels.
- **Quadratic Weighted Kappa (QWK):** An extension of Cohen’s Kappa that measures the agreement between predicted and true labels, applying a quadratic penalty to larger misclassifications (i.e., predictions farther from the true label are penalized more heavily).
---
## Citation
If you use BAREC in your work, please cite the following papers:
```
@inproceedings{elmadani-etal-2025-readability,
title = "A Large and Balanced Corpus for Fine-grained {A}rabic Readability Assessment",
author = "Elmadani, Khalid N. and
Habash, Nizar and
Taha-Thomure, Hanada",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.842/"
}
@inproceedings{habash-etal-2025-guidelines,
title = "Guidelines for Fine-grained Sentence-level {A}rabic Readability Annotation",
author = "Habash, Nizar and
Taha-Thomure, Hanada and
Elmadani, Khalid N. and
Zeino, Zeina and
Abushmaes, Abdallah",
booktitle = "Proceedings of the 19th Linguistic Annotation Workshop (LAW-XIX-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.law-1.30/"
}
``` |