Multilingual Dynamic Topic Model

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http://hdl.handle.net/10138/307837

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Zosa , E & Granroth-Wilding , M 2019 , Multilingual Dynamic Topic Model . in G Angelova , R Mitkov , I Nikolova & I Temnikova (eds) , RANLP 2019 - Natural Language Processing a Deep Learning World : Proceedings . International conference Recent advances in natural language processing , INCOMA , Shoumen , pp. 1388-1396 , Recent Advances in Natural Language Processing , Varna , Bulgaria , 02/09/2019 . https://doi.org/10.26615/978-954-452-056-4_159

Title: Multilingual Dynamic Topic Model
Author: Zosa, Elaine; Granroth-Wilding, Mark
Editor: Angelova, Galia; Mitkov, Ruslan; Nikolova, Ivelina; Temnikova, Irina
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Publisher: INCOMA
Date: 2019-09-04
Language: eng
Number of pages: 9
Belongs to series: RANLP 2019 - Natural Language Processing a Deep Learning World Proceedings
Belongs to series: International conference Recent advances in natural language processing
ISBN: 978-954-452-055-7
978-954-452-056-4
URI: http://hdl.handle.net/10138/307837
Abstract: Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data. Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture crosslingual topics that evolve across time. We present results of this model on a parallel German-English corpus of news articles and a comparable corpus of Finnish and Swedish news articles. We demonstrate the capability of ML-DTM to track significant events related to a topic and show that it finds distinct topics and performs as well as existing multilingual topic models in aligning cross-lingual topics.
Subject: 113 Computer and information sciences
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