Conference paper Open Access

Multilingual Dynamic Topic Model

Elaine Zosa; Mark Granroth-Wilding

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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  "description": "<p>Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data.<br>\nCurrent DTMs are applicable only to monolingual datasets. In this paper we present the multilingual<br>\ndynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual<br>\ntopic modeling method to capture crosslingual topics that evolve across time. We present<br>\nresults of this model on a parallel German-English corpus of news articles and a comparable corpus<br>\nof Finnish and Swedish news articles. We demonstrate&nbsp;the capability of ML-DTM to track significant<br>\nevents related to a topic and show that it finds&nbsp;distinct topics and performs as well as existing<br>\nmultilingual topic models in aligning cross-lingual&nbsp;topics.</p>", 
  "license": "", 
  "creator": [
      "affiliation": "University of Helsinki", 
      "@type": "Person", 
      "name": "Elaine Zosa"
      "affiliation": "University of Helsinki", 
      "@type": "Person", 
      "name": "Mark Granroth-Wilding"
  "headline": "Multilingual Dynamic Topic Model", 
  "image": "", 
  "datePublished": "2019-09-02", 
  "url": "", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "Topic modeling"
  "@context": "", 
  "identifier": "", 
  "@id": "", 
  "workFeatured": {
    "url": "", 
    "alternateName": "RANLP", 
    "location": "Bulgaria", 
    "@type": "Event", 
    "name": "Recent Advances in Natural Language Processing"
  "name": "Multilingual Dynamic Topic Model"
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