Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings
Abstract
We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering. Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents.
Cite
@article{arxiv.2101.11059,
title = {Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings},
author = {Kailash Karthik Saravanakumar and Miguel Ballesteros and Muthu Kumar Chandrasekaran and Kathleen McKeown},
journal= {arXiv preprint arXiv:2101.11059},
year = {2021}
}
Comments
To appear in Proceedings of The 16th Conference of the European Chapter of the Association for Computational Linguistics