publication . Conference object . Part of book or chapter of book . 2020

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;
Open Access
  • Published: 01 Nov 2020
  • Publisher: Springer
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 lin...
Persistent Identifiers
free text keywords: Apertium RDF, cross-lingual model transfer, Fintan, Linked data, Computer science, Natural language processing, computer.software_genre, computer, Artificial intelligence, business.industry, business, Cross lingual
Funded by
EC| Pret-a-LLOD
Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors
  • Funder: European Commission (EC)
  • Project Code: 825182
  • Funding stream: H2020 | RIA
Digital Humanities and Cultural Heritage
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Conference object . 2020
Provider: ZENODO
Part of book or chapter of book
Provider: UnpayWall
Part of book or chapter of book . 2020
Provider: Crossref
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