A Finnish news corpus for named entity recognition

Teemu Ruokolainen, Pekka Kauppinen, Miikka Silfverberg, Krister Lindén

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

We present a corpus of Finnish news articles with a manually prepared named entity annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source. The corpus is available for research purposes. We present baseline experiments on the corpus using a rule-based and two deep learning systems on two, in-domain and out-of-domain, test sets.
Originalspråkengelska
TidskriftLanguage Resources and Evaluation
Volym54
Utgåva1
Sidor (från-till)247-272
Antal sidor26
ISSN1574-0218
DOI
StatusPublicerad - mar 2020
MoE-publikationstypA1 Tidskriftsartikel-refererad

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