September 2, 2019 Conference paper Open Access

Elaine Zosa; Mark Granroth-Wilding

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    "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>", 
    "language": "eng", 
    "title": "Multilingual Dynamic Topic Model", 
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        "title": "NewsEye: A Digital Investigator for Historical Newspapers", 
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    "keywords": [
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    "publication_date": "2019-09-02", 
    "creators": [
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        "affiliation": "University of Helsinki", 
        "name": "Elaine Zosa"
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      {
        "affiliation": "University of Helsinki", 
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      "dates": "2-4 September 2019", 
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