Renard (Relationships Extraction from NARrative Documents) is a library for creating and using custom character networks extraction pipelines. Renard can extract dynamic as well as static character networks.
You can install the latest version using pip:
pip install renard-pipeline
Currently, Renard supports Python 3.8, 3.9 and 3.10.
Documentation, including installation instructions, can be found at https://compnet.github.io/Renard/
If you need local documentation, it can be generated using Sphinx. From the docs directory, make html should create documentation under docs/_build/html.
Renard's central concept is the Pipeline.A Pipeline is a list of PipelineStep that are run sequentially in order to extract a character graph from a document. Here is a simple example:
from renard.pipeline import Pipeline
from renard.pipeline.tokenization import NLTKTokenizer
from renard.pipeline.ner import NLTKNamedEntityRecognizer
from renard.pipeline.character_unification import GraphRulesCharacterUnifier
from renard.pipeline.graph_extraction import CoOccurrencesGraphExtractor
with open("./my_doc.txt") as f:
text = f.read()
pipeline = Pipeline(
[
NLTKTokenizer(),
NLTKNamedEntityRecognizer(),
GraphRulesCharacterUnifier(min_appearance=10),
CoOccurrencesGraphExtractor(co_occurrences_dist=25)
]
)
out = pipeline(text)For more information, see renard_tutorial.py, which is a tutorial in the jupytext format. You can open it as a notebook in Jupyter Notebook (or export it as a notebook with jupytext --to ipynb renard-tutorial.py).
Renard uses pytest for testing. To launch tests, use the following command :
poetry run python -m pytest tests
Expensive tests are disabled by default. These can be run by setting the environment variable RENARD_TEST_ALL to 1.
see the "Contributing" section of the documentation.
If you use Renard in your research project, please cite it as follows:
@Article{Amalvy2024,
doi = {10.21105/joss.06574},
year = {2024},
publisher = {The Open Journal},
volume = {9},
number = {98},
pages = {6574},
author = {Amalvy, A. and Labatut, V. and Dufour, R.},
title = {Renard: A Modular Pipeline for Extracting Character
Networks from Narrative Texts},
journal = {Journal of Open Source Software},
} We would be happy to hear about your usage of Renard, so don't hesitate to reach out!