---
language:
- ar
license: cc-by-sa-4.0
size_categories:
- 1K<n<10K
task_categories:
- token-classification
pretty_name: AraSeg
tags:
- sentence-segmentation
dataset_info:
- config_name: PA
  features:
  - name: doc_id
    dtype: string
  - name: doc_name
    dtype: string
  - name: source
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  - name: genre
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  - name: tokens
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  - name: labels
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  - name: paragraph_ids
    list: int64
  - name: sent_ids
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  - name: text
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  - name: label_str
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  splits:
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    num_examples: 1703
  - name: dev
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  - name: test
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  download_size: 12565904
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- config_name: NP
  features:
  - name: doc_id
    dtype: string
  - name: doc_name
    dtype: string
  - name: source
    dtype: string
  - name: genre
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  - name: tokens
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  - name: labels
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  - name: paragraph_ids
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  - name: sent_ids
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  - name: text
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- config_name: NoPnx-PA
  features:
  - name: doc_id
    dtype: string
  - name: doc_name
    dtype: string
  - name: source
    dtype: string
  - name: genre
    dtype: string
  - name: tokens
    list: string
  - name: labels
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  - name: paragraph_ids
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  - name: sent_ids
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  - name: text
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  - name: label_str
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- config_name: NoPnx-NP
  features:
  - name: doc_id
    dtype: string
  - name: doc_name
    dtype: string
  - name: source
    dtype: string
  - name: genre
    dtype: string
  - name: tokens
    list: string
  - name: labels
    list: int64
  - name: paragraph_ids
    list: int64
  - name: sent_ids
    list: int64
  - name: text
    dtype: string
  - name: label_str
    dtype: string
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  - name: dev
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  - name: test
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configs:
- config_name: PA
  default: true
  data_files:
  - split: train
    path: PA/train-*
  - split: dev
    path: PA/dev-*
  - split: test
    path: PA/test-*
- config_name: NP
  data_files:
  - split: train
    path: NP/train-*
  - split: dev
    path: NP/dev-*
  - split: test
    path: NP/test-*
- config_name: NoPnx-PA
  data_files:
  - split: train
    path: NoPnx-PA/train-*
  - split: dev
    path: NoPnx-PA/dev-*
  - split: test
    path: NoPnx-PA/test-*
- config_name: NoPnx-NP
  data_files:
  - split: train
    path: NoPnx-NP/train-*
  - split: dev
    path: NoPnx-NP/dev-*
  - split: test
    path: NoPnx-NP/test-*
---

# AraSeg

## Dataset Summary

**AraSeg** is a manually annotated, genre-diverse benchmark for **Arabic sentence segmentation** in
Modern Standard Arabic. It contains **2,187 documents** with **48,250 paragraphs** and **121,623
sentences** (about **1.45M tokens**), drawn from **30 sources** spanning **8 genres**: Children,
Education, Literature, Media, Poetry, Politics, Religion and Wiki.

Arabic punctuation is sparse, inconsistent and ambiguous, and many sentence boundaries are not
marked by punctuation at all: 39% of AraSeg sentence boundaries are word-based. AraSeg covers
punctuated and unpunctuated text, with and without paragraph structure, so segmentation can be
studied under realistic conditions. Most of the gold segmentations come from the CAMeL Treebank
and the BAREC corpus; the full Quran and additional Old and New Testament text were added to
broaden the religious genre.

---

## Supported Tasks

Sentence segmentation is framed as **binary token classification**: for every token, predict
whether a sentence boundary follows it. The dataset has four configs (task variants) that differ in
the availability of punctuation and paragraph boundaries:

| Config | Paragraph boundaries | Punctuation |
|---|---|---|
| `PA` (default) | ✓ | ✓ |
| `NP` | ✗ | ✓ |
| `NoPnx-PA` | ✓ | ✗ |
| `NoPnx-NP` | ✗ | ✗ |

- **PA (Paragraph-Aware):** paragraph boundaries are kept, as `"\n"` tokens between paragraphs.
- **NP (No-Paragraph):** each document is one stream of tokens.
- **NoPnx:** all punctuation and symbol characters are removed, along with source-specific
  boundary markers (Quran, Old and New Testament verse numbers; ArabicMMLU answer labels such as
  `( أ )`.

```python
from datasets import load_dataset

ds = load_dataset("MBZUAI/AraSEG", "NoPnx-NP")
```

---

### Languages

- **Arabic** (Modern Standard Arabic)

---

## Dataset Structure

### Data Instances

One row per document (lists truncated):

`{'doc_id': 'doc_4a2fa5a558fa', 'doc_name': 'ArabicMMLU_Group_10_001', 'source': 'ArabicMMLU', 'genre': 'Education', 'tokens': ['معنى', 'كلمة', 'صاغ', 'في', 'قول', 'الشاعر', 'صاغ', 'نصر', 'العرب', 'في', 'يوم', 'الكرامة', ...], 'labels': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...], 'paragraph_ids': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...], 'sent_ids': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...], 'text': 'معنى كلمة صاغ في قول الشاعر صاغ نصر العرب في يوم الكرامة ( أ ) صاغة ( ب ) سطع ( ج ) فزء ( د ) صنع \n معنى كلمة ضرب ...', 'label_str': '0000000000010001000100010001...'}`

### Data Fields

- **doc_id**: Stable document identifier (the same ids as the shared-task datasets).
- **doc_name**: Original document name.
- **source**: Source corpus of the document (e.g. `Majed`, `Hindawi`, `Quran`, `ArabicMMLU`).
- **genre**: One of `Children`, `Education`, `Literature`, `Media`, `Poetry`, `Politics`, `Religion`, `Wiki`.
- **tokens**: Whitespace tokens of the document (punctuation is already separated from words). In
  the PA configs, a `"\n"` token marks each paragraph boundary.
- **labels**: `1` if a sentence ends after the token, else `0`. `"\n"` tokens are always `0`.
- **paragraph_ids**: Paragraph id of each token. A `"\n"` token carries the id of the paragraph it closes.
- **sent_ids**: Sentence id within its paragraph for each token (`-1` for `"\n"` tokens).
- **text**: The tokens joined by single spaces.
- **label_str**: `labels` as a string of `0`s and `1`s, the format used for shared-task submissions.


### Data Splits

- AraSeg has three splits: *Train* (≈80%), *Dev* (≈10%) and *Test* (≈10%), by word count.
- The splits are at the document level, to prevent leakage.
- The original CAMeL Treebank and BAREC splits are preserved. The added Quran, Old Testament and
  New Testament documents were assigned to splits greedily, balancing word counts.
- All four configs contain the same documents in each split.

### Genre Statistics

| Genre | #Docs | #Paras | #Sents | #Tokens | Pnx Density | Avg. Sent. Len. |
|---|---|---|---|---|---|---|
| Children | 422 | 9.8K | 16.7K | 172.7K | 20.6% | 10.4 |
| Education | 605 | 18.8K | 50.3K | 373.3K | 25.3% | 7.4 |
| Literature | 406 | 8.0K | 21.0K | 339.4K | 14.2% | 16.2 |
| Media | 101 | 1.2K | 2.5K | 32.9K | 12.4% | 13.2 |
| Poetry | 30 | 1.7K | 1.7K | 11.7K | 7.7% | 6.9 |
| Politics | 17 | 5.9K | 9.7K | 147.0K | 10.6% | 15.1 |
| Religion | 438 | 1.4K | 13.7K | 216.0K | 16.1% | 15.7 |
| Wiki | 168 | 1.6K | 6.1K | 128.6K | 13.8% | 21.2 |

---

## Annotation

AraSeg re-uses the gold sentence segmentation of the CAMeL Treebank and BAREC, which follow the
CAMeL Treebank guidelines: automatic splitting on punctuation (`, . ; ? ! :`), followed by manual
correction through controlled splits and merges. Dependent or incomplete clauses are never
segmented into separate sentences. For the Quran, the Old and New Testament, and poetry, verse
boundaries are sentence boundaries; in poetry they are also paragraph boundaries. For sources
without explicit paragraphs, paragraphs follow the document structure (e.g. Quran chapters,
ArabicMMLU questions). In ArabicMMLU, the question and each answer option are separate sentences.

---

## Evaluation

We define sentence segmentation as a binary token classification task. The following metrics are
used for evaluation:

- **Boundary Precision (P):** The percentage of predicted sentence boundaries that match a reference
  boundary. Higher precision means fewer false boundary insertions.
- **Boundary Recall (R):** The percentage of reference sentence boundaries that are predicted.
  Higher recall means fewer missed boundaries.
- **Boundary F1 (F1):** The harmonic mean of boundary precision and recall.

A prediction is correct only if it exactly matches a gold boundary. Scores are computed per document
and macro-averaged over documents, so long documents do not dominate. The evaluation script is
available at https://github.com/mbzuai-nlp/araseg-shared-task-2026.
<!-- Code and models: https://github.com/mbzuai-nlp/araseg -->

---

## Citation

If you use AraSeg in your work, please cite the following paper:

```
@inproceedings{elkholy-etal-2026-arabic-sentence-segmentation,
    title = "{A}rabic Sentence Segmentation Across Genres and Punctuation Conditions",
    author = "Elkholy, Mohammed and
      Elmadani, Khalid N. and
      Habash, Nizar and
      Alhafni, Bashar",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics"
}
```