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Tagging a Norwegian Dialect Corpus

Kåsen, Andre; Hagen, Kristin; Nøklestad, Anders; Priestley, Joel
Chapter; PublishedVersion; Peer reviewed
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TaggingaNorwegianDialectCorpus.pdf (412.0Kb)
Year
2019
Permanent link
http://urn.nb.no/URN:NBN:no-79896

CRIStin
1791843

Is part of
Linköping Electronic Conference Proceedings
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Appears in the following Collection
  • Institutt for informatikk [3643]
  • Institutt for lingvistiske og nordiske studier [672]
  • CRIStin høstingsarkiv [16887]
Original version
Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). 2019, 350-355
Abstract
This paper describes an evaluation of five data-driven part-of-speech (PoS) taggers for spoken Norwegian. The taggers all rely on different machine learning mechanisms: decision trees, hidden Markov models (HMMs), conditional random fields (CRFs), long-short term memory networks (LSTMs), and convolutional neural networks (CNNs). We go into some of the challenges posed by the task of tagging spoken, as opposed to written, language, and in particular a wide range of dialects as is found in the recordings of the LIA (Language Infrastructure made Accessible) project. The results show that the taggers based on either conditional random fields or neural networks perform much better than the rest, with the LSTM tagger getting the highest score.
 
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