Renard: A Modular Pipeline for Extracting Character Networks from Narrative Texts

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Amalvy, Arthur, Labatut, Vincent, Dufour, Richard
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866914855898841088
author Amalvy, Arthur
Labatut, Vincent
Dufour, Richard
author_facet Amalvy, Arthur
Labatut, Vincent
Dufour, Richard
contents Renard (Relationships Extraction from NARrative Documents) is a Python library that allows users to define custom natural language processing (NLP) pipelines to extract character networks from narrative texts. Contrary to the few existing tools, Renard can extract dynamic networks, as well as the more common static networks. Renard pipelines are modular: users can choose the implementation of each NLP subtask needed to extract a character network. This allows users to specialize pipelines to particular types of texts and to study the impact of each subtask on the extracted network.
format Preprint
id arxiv_https___arxiv_org_abs_2407_02284
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Renard: A Modular Pipeline for Extracting Character Networks from Narrative Texts
Amalvy, Arthur
Labatut, Vincent
Dufour, Richard
Computation and Language
Renard (Relationships Extraction from NARrative Documents) is a Python library that allows users to define custom natural language processing (NLP) pipelines to extract character networks from narrative texts. Contrary to the few existing tools, Renard can extract dynamic networks, as well as the more common static networks. Renard pipelines are modular: users can choose the implementation of each NLP subtask needed to extract a character network. This allows users to specialize pipelines to particular types of texts and to study the impact of each subtask on the extracted network.
title Renard: A Modular Pipeline for Extracting Character Networks from Narrative Texts
topic Computation and Language
url https://arxiv.org/abs/2407.02284