LaTeCH-CLfL-2020

Repository associated with History to Myths: Social Network Analysis for Comparison of Stories over Time paper.

Citation

@inproceedings{besnier-2020-history,
    title = "History to Myths: Social Network Analysis for Comparison of Stories over Time",
    author = "Besnier, Cl{\'e}ment",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.1",
    pages = "1--9",
    abstract = {We discuss on how related stories can be compared by their characters. We investigate character graphs, or social networks, in order to measure evolution of character importance over time. To illustrate this, we chose the Siegfried-Sigurd myth that may come from a Merovingian king named Sigiberthus. The Nibelungenlied, the V{\"o}lsunga saga and the History of the Franks are the three resources used.},
}

Data

Texts:

• Decem libros historium (DLH) by Gregory of Tours
• Nibelungenlied (NIB)
• Völsunga saga (VOL)

DLH is the historical reference. NIB and VÖL are fiction works.

Installation

Tested on Windows 10 and Ubuntu 16.04. Tested with Python 3.7 and 3.8.

Install with pip

$ pip install latechclfl2020besnier

or download source

$ git clone https://github.com/clemsciences/LaTeCH-CLfl-2020-besnier.git
$ cd LaTeCH-CLfl-2020-besnier
$ virtualenv -p /usr/bin/python3 venv
$ source venv/bin/activate
$ pip install -r requirements.txt 

Reproducing results

1. Download resources Run $ python -m -m latechclfl2020.models.initiate latechclfl2020/models/initiate.py
2. Generating graphs. Run $ python -m latechclfl2020.models.scripts latechclfl2020/models/scripts.py
3. Generating character feature table in paper. Run $ python -m latechclfl2020.models.reconstruction latechclfl2020/models/reconstruction.py
4. Generating Brynhildr ego-graphs. Run $ python -m latechclfl2020.models.paper.graph_visualisation latechclfl2020/models/paper/graph_visualisation.py
5. Corpus observation. Run $ python -m latechclfl2020.models.paper.corpus_observation latechclfl2020/models/paper/corpus_observation.py