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Relationships Extraction from NARrative Documents

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Renard

DOI

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.

The Renard logo

Installation

You can install the latest version using pip:

pip install renard-pipeline

Currently, Renard supports Python 3.8, 3.9 and 3.10.

Documentation

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.

Tutorial

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).

Running tests

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.

Contributing

see the "Contributing" section of the documentation.

How to cite

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!