Presentation Open Access

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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{
  "DOI": "10.5281/zenodo.1149023", 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Schl\u00f6gl, Matthias"
    }, 
    {
      "family": "Lejtovicz, Katalin"
    }, 
    {
      "family": "Bern\u00e1d, \u00c1goston Z\u00e9n\u00f3"
    }, 
    {
      "family": "Kaiser, Maximilian"
    }, 
    {
      "family": "Rumpolt, Peter"
    }
  ], 
  "id": "1149023", 
  "issued": {
    "date-parts": [
      [
        2018, 
        1, 
        16
      ]
    ]
  }, 
  "language": "eng", 
  "note": "Interactive version of this presentation is available here: https://apis.acdh.oeaw.ac.at/presentation_innsbruck17/", 
  "publisher": "Zenodo", 
  "title": "Using deep learning to explore movement of people in a large corpus of  biographies", 
  "type": "speech"
}
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