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In:The Swedish FrameNet++: Harmonization, integration, method development and practical language technology applications
Edited by Dana Dannélls, Lars Borin and Karin Friberg Heppin
[Natural Language Processing 14] 2021
► pp. 97–122

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Chapter 4
A lexical resource for computational historical linguistics

 | University of Gothenburg
 | University of Borås | University of Gothenburg
 | University of Gothenburg
 | University of Gothenburg
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Available under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 license.

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Published online: 26 November 2021
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https://doi.org/10.1075/nlp.14.04ade
Abstract
In this chapter we present the diachronic dimension of Swedish FrameNet++. We describe the historical lexical resources currently available for Swedish, linked to the Contemporary Swedish lexicon Saldo. We present a case study of how interlinking the dictionaries simultaneously allows us to study lexical change. We also present a method of linking text words to lexicon entries, facilitating interactive exploration of historical texts. Diachronical language resources present both a high-variation challenge from a wider language technology perspective, and an interesting object of linguistic study. While a number of improvements of the parts of the diachronic lexical macroresource are still needed, this resource is invaluable for analysing and accessing historical texts, as well as for both synchronic historical and diachronic lexical studies.
Article outline
  • 1.Introduction
  • 2.A brief overview of Swedish language stages
  • 3.Diachronical lexical resources
    • 3.1Adding diachronical lexicons to SweFN++
    • 3.2A lexical resource for Late Modern Swedish
    • 3.3A lexical resource for Early Modern Swedish
    • 3.4A lexical resource for Old Swedish
  • 4.Diapivot
    • 4.1Methods of automatically linking lexical resources
    • 4.2An application: Studying lexical change and grammaticalization
  • 5.Spelling variation and linking texts to lexicons
    • 5.1A noisy channel approach to lemmatization
    • 5.2Training a model on dictionary data
    • 5.3Evaluation
    • 5.4An application: FSvReader
  • 6.Conclusions
  • Notes
  • References
  • Appendix
References (36)
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