Multilingual Dynamic Topic Model

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Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationRANLP 2019 - Natural Language Processing a Deep Learning World : Proceedings
RedaktörerGalia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova
Antal sidor9
UtgivningsortShoumen
FörlagINCOMA
Utgivningsdatum4 sep 2019
Sidor1388-1396
ISBN (tryckt)978-954-452-055-7
ISBN (elektroniskt)978-954-452-056-4
DOI
StatusPublicerad - 4 sep 2019
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangRecent Advances in Natural Language Processing - Varna, Bulgarien
Varaktighet: 2 sep 20194 sep 2019

Publikationsserier

NamnInternational conference Recent advances in natural language processing
ISSN (tryckt)1313-8502
ISSN (elektroniskt)2603-2813

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

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