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--- |
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dataset_info: |
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- config_name: alias-resolution |
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features: |
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- name: form |
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dtype: string |
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- name: type |
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dtype: string |
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- name: mentions |
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dtype: int64 |
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- name: entity |
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dtype: string |
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- name: novel |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 395702 |
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num_examples: 5985 |
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download_size: 117587 |
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dataset_size: 395702 |
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- config_name: text |
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features: |
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- name: tokens |
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sequence: string |
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- name: novel |
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dtype: string |
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|
splits: |
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|
- name: train |
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|
num_bytes: 10400464 |
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num_examples: 7 |
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download_size: 2673188 |
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dataset_size: 10400464 |
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|
configs: |
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- config_name: alias-resolution |
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data_files: |
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- split: train |
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path: alias-resolution/train-* |
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- config_name: text |
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data_files: |
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- split: train |
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path: text/train-* |
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--- |
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# 7-romans |
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This dataset contains 7 French novels, entirely annoted for the alias resolution task. See the related [NER dataset](https://huggingface.co/datasets/compnet-renard/7-romans-ner). |
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| **Novel** | **Author** | **Publication Year** | **Number of tokens** | **Number of characters** | |
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|-------------------------|-------------------|--------------------------|----------------------|---------------------------| |
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| Les Trois Mousquetaires | Alexandre Dumas | 1849 | 294 989 | 213 | |
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| Le Rouge et le Noir | Stendhal | 1854 | 216 445 | 318 | |
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| Eugénie Grandet | Honoré de Balzac | 1855 | 80 659 | 107 | |
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| Germinal | Émile Zola | 1885 | 220 273 | 102 | |
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| Bel-Ami | Guy de Maupassant | 1901 | 138 156 | 150 | |
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| Notre-Dame de Paris | Victor Hugo | 1904 | 221 351 | 536 | |
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| Madame Bovary | Gustave Flaubert | 1910 | 148 861 | 175 | |
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This gold standard corpus was created in the context of a project at the ObTIC laboratory, Sorbonne University. The project was directed by Motasem Alrahabi, and annnotations were performed by Perrine Maurel, Una Faller and Romaric Parnasse. |
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The corpus was then used to train a [CamemBERT NER model](https://huggingface.co/compnet-renard/camembert-base-literary-NER-v2) in collaboration with Arthur Amalvy and Vincent Labatut, from Avignon University. |
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# Usage |
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To load the alias resolution data: |
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```python |
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>>> from datasets import load_dataset |
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>>> dataset = load_dataset("compnet-renard/7-romans-alias-resolution", "alias-resolution") |
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>>> dataset["train"][0] |
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{'form': 'À la belle vue', 'type': 'LOC', 'mentions': 1, 'entity': '?', 'novel': 'BelAmi'} |
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``` |
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Only the PER entities are annotated: other types only have a "?" in their entity field. |
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The novel texts themselves are in a separate configuration: |
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```python |
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>>> dataset = load_dataset("compnet-renard/7-romans-alias-resolution", "text") |
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>>> dataset["train"].features |
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{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'novel': Value(dtype='string', id=None)} |
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``` |
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# Citation |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@InProceedings{Maurel2025, |
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authors = {Maurel, P. and Amalvy, A. and Labatut, V. and Alrahabi, M.}, |
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title = {Du repérage à l’analyse : un modèle pour la reconnaissance d’entités nommées dans les textes littéraires en français}, |
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booktitle = {Digital Humanities 2025}, |
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year = {2025}, |
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} |
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``` |
