Multilingual Clustering of Streaming News

Sebastiao Miranda; Arturs Znotins; Shay Cohen; Guntis Barzdins

Multilingual Clustering of Streaming News

Sebastiao Miranda, Arturs Znotins, Shay Cohen, Guntis Barzdins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	2018 Conference on Empirical Methods in Natural Language Processing
Place of Publication	Brussels, Belgium
Publisher	Association for Computational Linguistics
Pages	4535-4544
Number of pages	10
Publication status	Published - Nov 2018
Event	2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium Duration: 31 Oct 2018 → 4 Nov 2018 http://emnlp2018.org/

Conference

Conference	2018 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2018
Country	Belgium
City	Brussels
Period	31/10/18 → 4/11/18
Internet address	http://emnlp2018.org/

Access to Document

multilingual clustering of streaming newsAccepted author manuscript, 387 KB
multilingual clustering of streaming newsFinal published version, 478 KBLicence: Creative Commons: Attribution (CC-BY)

1 Finished

SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
EU government bodies
1/02/16 → 31/01/19
Project: Research

Cite this

@inproceedings{675eb87ef6424a1790a0a4022b330690,

title = "Multilingual Clustering of Streaming News",

abstract = "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.",

author = "Sebastiao Miranda and Arturs Znotins and Shay Cohen and Guntis Barzdins",

year = "2018",

month = nov,

language = "English",

pages = "4535--4544",

booktitle = "2018 Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics",

note = "2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 ; Conference date: 31-10-2018 Through 04-11-2018",

url = "http://emnlp2018.org/",

}

Miranda, S, Znotins, A, Cohen, S & Barzdins, G 2018, Multilingual Clustering of Streaming News. in 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, pp. 4535-4544, 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31/10/18. <http://aclweb.org/anthology/D18-1483 >

TY - GEN

T1 - Multilingual Clustering of Streaming News

AU - Miranda, Sebastiao

AU - Znotins, Arturs

AU - Cohen, Shay

AU - Barzdins, Guntis

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 4535

EP - 4544

BT - 2018 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

CY - Brussels, Belgium

T2 - 2018 Conference on Empirical Methods in Natural Language Processing

Y2 - 31 October 2018 through 4 November 2018

ER -

Multilingual Clustering of Streaming News

Abstract

Conference

Access to Document

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Projects

SUMMA - Scalable Understanding of Mulitingual Media

Cite this