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Using deep learning to explore movement of people in a large corpus of biographies

Schlögl, Matthias; Lejtovicz, Katalin; Bernád, Ágoston Zénó; Kaiser, Maximilian; Rumpolt, Peter


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{
  "@context": "https://schema.org/", 
  "@id": "https://doi.org/10.5281/zenodo.1149023", 
  "@type": "PresentationDigitalDocument", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0003-1451-0987", 
      "@type": "Person", 
      "affiliation": "Austrian Academy of Sciences", 
      "name": "Schl\u00f6gl, Matthias"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Austrian Academy of Sciences", 
      "name": "Lejtovicz, Katalin"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Austrian Academy of Sciences", 
      "name": "Bern\u00e1d, \u00c1goston Z\u00e9n\u00f3"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Austrian Academy of Sciences", 
      "name": "Kaiser, Maximilian"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Austrian Academy of Sciences", 
      "name": "Rumpolt, Peter"
    }
  ], 
  "datePublished": "2018-01-16", 
  "description": "<p>In this presentation we showcase our first experiences with deep learning models for relation extraction in german biographies. These models are trained on human annotations of relations between the biographed person and entities found in the full-text (e.g. person A &gt;&gt; travelled to &gt;&gt; Wien).</p>\n\n<p>An interactive version of this presentation that allows also to test the trained model can be found <a href=\"https://apis.acdh.oeaw.ac.at/presentation_innsbruck17/\">here</a>.</p>", 
  "identifier": "https://doi.org/10.5281/zenodo.1149023", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "name": "Using deep learning to explore movement of people in a large corpus of  biographies", 
  "url": "https://zenodo.org/record/1149023"
}
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