2018/09/03 by Sebastião Miranda, Artūrs Znotiņš, Miranda, Sebastião +5 · 1 citation
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Topic Modeling #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.1809.00540
openalex publication_date 2018/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual story clusters. Unlike typical clustering approaches that consider a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.