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-0.6.3.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 83448e66d1ea88949f36242bcf916911fd177e35336f1689ff4d0e42591f2d3e |
|
MD5 | eb51643a4e180f625f2fc32f85deafa2 |
|
BLAKE2b-256 | ed14057ab988a2e1b35c71a191f31a354ab46cfb8473fc8504962b7329d094e3 |
Hashes for taranis_story_clustering-0.6.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 503a8a0f0b5675ed254b230d50be55d149188c3b6dc4192ee1f98aef283d0e42 |
|
MD5 | 00d47212fd99d82f4803df621c8d318c |
|
BLAKE2b-256 | 4238c2fa6b9fb1cd2277d0a19deae799358215f9db9afc6ce5c0d557ca058d07 |