Andre Kåsen
Department of Informatics, University of Oslo, Norway
Kristin Hagen
The Text Laboratory, University of Oslo, Norway
Anders Nøklestad
The Text Laboratory, University of Oslo, Norway
Joel Priestley
The Text Laboratory, University of Oslo, Norway
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Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:40, s. 350--355
NEALT Proceedings Series 42:40, p. 350--355
Publicerad: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
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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