Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

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Abstract

Named Entity Recognition (NER) has greatly advanced by the introduction
of deep neural architectures. However, the success of these methods
depends on large amounts of training data. The scarcity of publicly available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for
Danish, evaluates its complementarity to limited gold data, and sheds light on
performance of Danish NER.
Original languageEnglish
Title of host publicationProceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19) .
PublisherAssociation for Computational Linguistics
Publication date2019
ISBN (Electronic)978-91-7929-995-8
Publication statusPublished - 2019
SeriesNEALT (Northern European Association of Language Technology) Proceedings Series
ISSN1736-6305

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