Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Languages:
Arabic
Size:
1K - 10K
Tags:
sentence-segmentation
License:
| 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 | |
| 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 | |
| splits: | |
| - name: train | |
| num_bytes: 52720724 | |
| num_examples: 1703 | |
| - name: dev | |
| num_bytes: 7377031 | |
| num_examples: 222 | |
| - name: test | |
| num_bytes: 7120149 | |
| num_examples: 262 | |
| download_size: 12565904 | |
| dataset_size: 67217904 | |
| - config_name: 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 | |
| splits: | |
| - name: train | |
| num_bytes: 51554132 | |
| num_examples: 1703 | |
| - name: dev | |
| num_bytes: 7222023 | |
| num_examples: 222 | |
| - name: test | |
| num_bytes: 6967733 | |
| num_examples: 262 | |
| download_size: 12394241 | |
| dataset_size: 65743888 | |
| - 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 | |
| list: int64 | |
| - name: paragraph_ids | |
| list: int64 | |
| - name: sent_ids | |
| list: int64 | |
| - name: text | |
| dtype: string | |
| - name: label_str | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 45099420 | |
| num_examples: 1703 | |
| - name: dev | |
| num_bytes: 6372675 | |
| num_examples: 222 | |
| - name: test | |
| num_bytes: 6132379 | |
| num_examples: 262 | |
| download_size: 11894149 | |
| dataset_size: 57604474 | |
| - 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 | |
| splits: | |
| - name: train | |
| num_bytes: 44007228 | |
| num_examples: 1703 | |
| - name: dev | |
| num_bytes: 6217955 | |
| num_examples: 222 | |
| - name: test | |
| num_bytes: 5985019 | |
| num_examples: 262 | |
| download_size: 11666727 | |
| dataset_size: 56210202 | |
| 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" | |
| } | |
| ``` | |