Metadata-Version: 2.1
Name: latechclfl2020besnier
Version: 1.0.6
Summary: UNKNOWN
Home-page: https://www.clementbesnier.fr/dlh-vol-nib
Author: Clément Besnier
Author-email: clem@clementbesnier.fr
License: Creative Commons Attribution 4.0 International Licence
Description: # LaTeCH-CLfL-2020
        
        ![PyPI](https://img.shields.io/pypi/v/latechclfl2020besnier)
        
        Repository associated with [*History to Myths: Social Network Analysis for Comparison of Stories over Time* paper](https://www.aclweb.org/anthology/2020.latechclfl-1.1/).
        
        ## 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        **DLH** is the historical reference.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
        ```shell script
        $ pip install latechclfl2020besnier
        ```
        
        or download source 
        
        ```shell script
        $ 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`
        
        
        
Keywords: old-norse,middle-high-german,latin,network analysis
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
