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Part of Speech Tagging: Shallow or Deep Learning?
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0002-6027-4156
2018 (English)In: Northern European Journal of Language Technology (NEJLT), ISSN 2000-1533, Vol. 5, no 1, p. 1-15Article in journal (Refereed) Published
Abstract [en]

Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.

Place, publisher, year, edition, pages
2018. Vol. 5, no 1, p. 1-15
Keywords [en]
part of speech tagging, deep learning, machine learning, multilingual nlp
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:su:diva-159759DOI: 10.3384/nejlt.2000-1533.1851OAI: oai:DiVA.org:su-159759DiVA, id: diva2:1245584
Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-09-14Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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  • de-DE
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  • nn-NO
  • nn-NB
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