Conference paper Open Access

Multilingual Dynamic Topic Model

Elaine Zosa; Mark Granroth-Wilding


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        <foaf:name>Elaine Zosa</foaf:name>
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        <foaf:name>Mark Granroth-Wilding</foaf:name>
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    <dct:title>Multilingual Dynamic Topic Model</dct:title>
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    <dcat:keyword>Topic modeling</dcat:keyword>
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    <dct:description>&lt;p&gt;Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data.&lt;br&gt; Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual&lt;br&gt; dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual&lt;br&gt; topic modeling method to capture crosslingual topics that evolve across time. We present&lt;br&gt; results of this model on a parallel German-English corpus of news articles and a comparable corpus&lt;br&gt; of Finnish and Swedish news articles. We demonstrate&amp;nbsp;the capability of ML-DTM to track significant&lt;br&gt; events related to a topic and show that it finds&amp;nbsp;distinct topics and performs as well as existing&lt;br&gt; multilingual topic models in aligning cross-lingual&amp;nbsp;topics.&lt;/p&gt;</dct:description>
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    <dct:title>NewsEye: A Digital Investigator for Historical Newspapers</dct:title>
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