Correlation Clustering for Crosslingual Link Detection
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1744-1748.
Abstract
The crosslingual link detection problem calls for identifying news articles in multiple languages that report on the same news event. This paper presents a novel approach based on constrained clustering. We discuss a general way for constrained clustering using a recent, graph-based clustering framework called correlation clustering. We introduce a correlation clustering implementation that features linear program chunking to allow processing larger datasets. We show how to apply the correlation clustering algorithm to the crosslingual link detection problem and present experimental results that show correlation clustering improves upon the hierarchical clustering approaches commonly used in link detection, and, hierarchical clustering approaches that take constraints into account.
@inproceedings{ijcai2007p282,
title = {Correlation Clustering for Crosslingual Link Detection},
author = {Van Gael, Jurgen and Zhu, Xiaojin},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1744--1748},
year = {2007},
url = {https://ijcai.org/proceedings/2007/282},
}
TY - CPAPER TI - Correlation Clustering for Crosslingual Link Detection AU - Van Gael, Jurgen AU - Zhu, Xiaojin T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1744 EP - 1748 UR - https://ijcai.org/proceedings/2007/282 ER -Copied
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