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  • GU-ISS Forskningsrapporter från Institutionen för svenska språket (2011-)
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  •   Startsida
  • Faculty of Humanities / Humanistiska fakulteten
  • Department of Swedish / Institutionen för svenska språket (-2021)
  • GU-ISS Forskningsrapporter från Institutionen för svenska språket (2011-)
  • Redigera dokument
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Assessing the quality of Språkbanken’s annotations

Sammanfattning
Most of the corpora in Språkbanken Text consist of unannotated plain text, such as almost all newspaper texts, social media texts, novels and official documents. We also have some corpora that are manually annotated in different ways, such as Talbanken (annotated for part-of-speech and syntactic structure), and the Stockholm Umeå Corpus (annotated for part-of-speech). Språkbanken's annotation pipeline Sparv aims to automatise the work of automatically annotating all our corpora, while still keeping the manual annotations intact. When all corpora are annotated, they can be made available, e.g., in the corpus searh tools Korp and Strix. Until now there has not been any comprehensive overview of the annotation tools and models that Sparv has been using for the last eight years. Some of them have not been updated since the start, such as the part-of-speech tagger Hunpos and the dependency parser MaltParser. There are also annotation tools that we still have not included, such as a constituency-based parser. Therefore Språkbanken initiated a project with the aim of conducting such an overview. This document is the outcome of that project, and it contains descriptions of the types of manual and automatic annotations that we currently have in Språkbanken, as well as an incomplete overview of the state-of-the-art with regards to annotation tools and models.
URL:
http://hdl.handle.net/2077/60417
Samlingar
  • GU-ISS Forskningsrapporter från Institutionen för svenska språket (2011-)
Fil(er)
gupea_2077_60417_4.pdf (397.0Kb)
Datum
2019-06-10
Författare
Ljunglöf, Peter
Zechner, Niklas
Nieto Piña, Luis
Adesam, Yvonne
Borin, Lars
Publikationstyp
report
ISSN
1401-5919
Serie/rapportnr.
GU-ISS
2019-03
Språk
eng
Metadata
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