Conference paper Open Access

Multilingual Dynamic Topic Model

Elaine Zosa; Mark Granroth-Wilding


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3402878", 
  "language": "eng", 
  "title": "Multilingual Dynamic Topic Model", 
  "issued": {
    "date-parts": [
      [
        2019, 
        9, 
        2
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Elaine Zosa"
    }, 
    {
      "family": "Mark Granroth-Wilding"
    }
  ], 
  "id": "3402878", 
  "event-place": "Bulgaria", 
  "type": "paper-conference", 
  "event": "Recent Advances in Natural Language Processing (RANLP)"
}
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