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 .
Development
pip install .[dev]
Use
See notebook\test_story_clustering.ipynb
for examples on how to use the clustering methods.
License
EUROPEAN UNION PUBLIC LICENCE v. 1.2
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for taranis_story_clustering-0.7.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 481a645dbec6f0b337916232d8d9f0b70c967dc3e558fab52a1f61a65502b263 |
|
MD5 | 4cddb00c142e822e3488ceef7339ad62 |
|
BLAKE2b-256 | 3b42b5842242fdd3aea88059ac9166627cce7332a4191c468daf3ff9793bddb9 |
Hashes for taranis_story_clustering-0.7.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4f91d2f1cce5fdaecbe2f578a8692f938cf76b41c76e87bcd6759ef672844821 |
|
MD5 | fa7f073946e7d7e500748f3492ac25cd |
|
BLAKE2b-256 | 6477c38136dda89735a3aa780bc13970f2924618b933ea3b221b2f942c9109e7 |