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