https://github.com/alexduvalinho/dynamic-graph-networks
Fine-grained dynamic graph for novel comprehension
https://github.com/alexduvalinho/dynamic-graph-networks
Last synced: 6 months ago
JSON representation
Fine-grained dynamic graph for novel comprehension
- Host: GitHub
- URL: https://github.com/alexduvalinho/dynamic-graph-networks
- Owner: AlexDuvalinho
- Created: 2020-05-12T16:14:14.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-06T20:09:25.000Z (over 4 years ago)
- Last Synced: 2025-04-01T21:15:58.358Z (6 months ago)
- Language: Python
- Homepage:
- Size: 34.8 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
## Context
This project has been realized for the Network Science Analytics class (MSc AI 2019-2020 at CentraleSupelec) by :
- Gaël de Léséleuc
- Alexandre Duval
The aim of our project was to conceive a pipeline that would extract a dynamic narrative graph from a given novels and then used this graph to study the narrative story of the novel.
A detailed report can be found in the *Report* folder.
## Installation
To install and use our end-to-end processing pipeline : from novel to narrative graph, use the following command in a new environment.
```
git clone https://github.com/dldk-gael/novel_story_building.git
cd novel_story_building
pip install -r requirements.txt
```
Our pipeline use BERT-NER in order to recognize the character names in the text. As a result, if you want to use it, you must [download](https://1drv.ms/u/s!Auc3VRul9wo5hghurzE47bTRyUeR?e=08seO3) the weight of pre-trained BERT-NER model and save it in the *models* folder.
Note that we have upload some cache data in the *data* folder so that you can test our pipeline without having to preprocess raw data from scratch.
## Pipeline structure
The folder has the following organization :
- *models* : to store the weight of BERT-NER model
- *data*: to store the data
- *raw_text* : **put here the novel.txt you want to analyse**
- *book_by_chapter* : will contain the novel split by chapter
- *coref_rules* : contains database that are use by coreference rules module
- *entity_list* : will contain the output of text processing as pickle file (ie: the occurence list)
- *graph* : will contain the narrative graph
- *report*: contains the project assignment description, our project proposal, our project report
- *src* is divide in three modules :
- *third_party* : contains source code of other frameworks we used such as BERT-NER and chapterize
- *text_processing* : the module responsible of text processing
- *graph* : the module responsible of graph creation and graph analysis
## Utilisation
To process a book and obtain different narrative graph as .gexf files, store the file book_name.txt in data/raw_test/ and launch the command :
```
python main.py --book book_name
```
By default, if you launch :
```
python main.py
```
it will re-run some graph analysis on HarryPotter book using cache data we left in the data folder.