renard-pipeline 0.6.5

Relationships Extraction from NARrative Documents

Renard

Renard (Relationship 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.

Installation

You can install the latest version using pip:

pip install renard-pipeline

Currently, Renard supports Python>=3.9,<=3.12

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 :

uv 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!