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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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- token-classification |
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- feature-extraction |
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language: |
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- ar |
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- fr |
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- en |
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tags: |
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- darija |
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- dz |
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- arabizi |
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- entity-linking |
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- el |
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- ner |
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- named-entity-recognation |
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- wikidata |
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- 2m |
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- algerian |
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- algeria |
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- dialect |
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size_categories: |
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- 1M<n<10M |
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--- |
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# ELNER-DZ: Algerian Arabic Dataset for Named Entity Recognition and Entity Linking |
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This dataset, titled **ELNER-DZ**, was created by **Bouguettoucha Hadjer Hanine** and **Djouablia Ilhem** as part of our Master’s thesis . It is the **first large-scale dataset** designed for **Named Entity Recognition (NER)** and **Entity Linking (EL)** in **Algerian Arabic Dialect** (Darija), including both **Arabic script** and **Arabizi** (Latin-script). |
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This dataset contains over **2 million dialectal sentences** labeled with more than **1.9 million named entities** and linked to **Wikidata QIDs**. |
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--- |
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## 🧾 Dataset Summary |
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- **Name**: ELNER-DZ |
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- **Languages**: Arabic (`ar`{For Modern Standard Arabic}, `arq`{For dialectal Arabic}), Arabizi (Latin), French (`fr`), English (`en`) |
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- **Script**: Arabic script and Latin script (Arabizi) |
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- **Format**: JSON (single file `data.json`) |
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- **Annotations**: |
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- Named Entity spans (start, end) |
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- Entity labels (PER, LOC, ORG, PROD, EVENT, LANG, LAW, MISC, DATE, NORP) |
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- Wikidata IDs |
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- Normalized canonical forms |
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--- |
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## 📁 File Structure |
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The dataset is provided as a single JSON file: |
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- `data.json`: A list of annotated examples. Each entry includes: |
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- `id`: Unique ID of the sentence |
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- `text`: Sentence string (Darija in Arabic or Arabizi) |
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- `entities`: List of dictionaries with: |
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- `start`, `end`: Character offsets of the entity span |
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- `label`: NER label (PER, LOC, ORG, etc.) |
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- `normalized`: Canonical surface form |
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- `wikidata_id`: Corresponding QID from Wikidata |
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--- |
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## ✨ Example |
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```json |
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{ |
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"id": 188, |
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"text": "3reft wa7ed lperson khadem f Yassir", |
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"entities": [ |
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{ |
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"start": 29, |
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"end": 35, |
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"label": "ORG", |
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"wikidata_id": "Q117156470", |
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"normalized": "Yassir" |
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} |
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] |
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} |
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``` |
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## 🏷️ Entity Types |
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The dataset includes the following entity types (NER labels): |
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* `PER`: Person |
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* `LOC`: Location |
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* `ORG`: Organization |
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* `PROD`: Product |
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* `LAW`: Legal concepts or texts |
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* `LANG`: Language |
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* `EVENT`: Events |
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* `DATE`: Dates |
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* `NORP`: Nationality, Religious, or Political groups |
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* `MISC`: Miscellaneous |
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* `SPORT`: Sports and Competitions |
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* `SYMPTOM`, `DISEASE`: Medical categories |
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--- |
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## 🧪 Tasks Supported |
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This dataset can be used for: |
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* Named Entity Recognition (NER) |
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* Entity Linking (EL) with Wikidata |
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* Dialectal NLP in Algerian Arabic |
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* Multilingual and multiscript NER modeling |
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* Pretraining or fine-tuning low-resource models |
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--- |
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## 🧰 How to Use |
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Install 🤗 Datasets and load the JSON file: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("HadjerHaninebgt7878/ELNER-DZ", data_files="data.json", split="train") |
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print(dataset[0]) |
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``` |
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--- |
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## 🔍 Dataset Details |
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* **Source**: Social media, forums, dialogues, e-commerce, Wikidata SPARQL |
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* **Annotation**: |
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* Manual and semi-automatic annotation |
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* Entity normalization |
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* Linking to Wikidata (QIDs) |
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--- |
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## 👩💻 Authors |
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* **Bouguettoucha Hadjer Hanine** |
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* **Djouablia Ilhem** |
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--- |
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## 📄 License |
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This dataset is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. |
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You are free to share, copy, redistribute, adapt, and build upon the material for any purpose, even commercially, **as long as you give appropriate credit**. |
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🔗 [View full license](https://creativecommons.org/licenses/by/4.0/) |
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--- |
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## 📚 Citation |
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Please cite this dataset as: |
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```bibtex |
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@dataset{elnerdz2025, |
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author = {Bouguettoucha, Hadjer Hanine and Djouablia, Ilhem}, |
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title = {ELNER-DZ: Algerian Arabic Dataset for Named Entity Recognition and Entity Linking}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/datasets/HadjerHaninebgt7878/ELNER-DZ}}, |
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note = {Dataset created as part of Master’s Thesis } |
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} |
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``` |
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``` |