Story Clustering Bot for Taranis-NG
Project description
Story Clustering
This code takes newsitems in the format as provided by Taranis-NG and clusters them into Stories.
Description and Use
The approach supports the following functionalities:
- Automatically detect Events.
- News items are clustered based on the detected Events.
- Documents belonging to related Events are then clustered into Stories.
Initial clustering
The method initial_clustering
in clustering.py
takes as input a dictionary of news_items_aggregate
(see tests/testdapa.py
for the actual input format) and outputs a dictionary containing two keys:
("event_clusters" : list of list of documents ids) and
("story_clusters" : list of list of documents ids)
Incremental clustering
The incremental clustering method takes as input a dictionary of news_items_aggregate
, containing new news items to be clustered, and clustered_news_items_aggregate
, containing already clustered items, and tries to cluster the new documents to the existing clusters or create new ones. See tests/testdata.py
for the actual input formats. This method also
outputs a dictionary containing two keys:
("event_clusters" : list of list of documents ids) and
("story_clusters" : list of list of documents ids)
Installation
The requirements.txt
file should list all Python libraries that the story-clustering
depends on, and they will be installed using:
pip install -r requirements.txt
Use
See notebook\test_story_clustering.ipynb
for examples on how to use the clustering methods.
License
EUROPEAN UNION PUBLIC LICENCE v. 1.2
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