Conference paper Open Access

Leveraging Linguistic Linked Data for Cross-Lingual Model Transfer in the Pharmaceutical Domain

Jorge Gracia; Christian Fäth; Matthias Hartung; Max Ionov; Julia Bosque-Gil; Susana Veríssimo; Christian Chiarcos; Matthias Orlikowski

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Jorge Gracia</dc:creator>
  <dc:creator>Christian Fäth</dc:creator>
  <dc:creator>Matthias Hartung</dc:creator>
  <dc:creator>Max Ionov</dc:creator>
  <dc:creator>Julia Bosque-Gil</dc:creator>
  <dc:creator>Susana Veríssimo</dc:creator>
  <dc:creator>Christian Chiarcos</dc:creator>
  <dc:creator>Matthias Orlikowski</dc:creator>
  <dc:description>We describe the use of linguistic linked data to support a cross-lingual transfer framework for sentiment analysis in the pharmaceutical domain. The proposed system dynamically gathers translations from the Linked Open Data (LOD) cloud, particularly from Apertium RDF, in order to project a deep learning-based sentiment classifier from one language to another, thus enabling scalability and avoiding the need of model re-training when transferred across languages. We describe the whole pipeline traversed by the multilingual data, from their conversion into RDF based on a new dynamic and flexible transformation framework, through their linking and publication as linked data, and finally their exploitation in the particular use case. Based on experiments on projecting a sentiment classifier from English to Spanish, we demonstrate how linked data techniques are able to enhance the multilingual capabilities of a deep learning-based approach in a dynamic and scalable way, in a real application scenario from the pharmaceutical domain.</dc:description>
  <dc:subject>Apertium RDF, cross-lingual model transfer, Fintan</dc:subject>
  <dc:title>Leveraging Linguistic Linked Data for Cross-Lingual Model Transfer in the Pharmaceutical Domain</dc:title>
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