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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. 169–190

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Chapter 7
NLP for resource building

 | 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.14.07joh
Abstract
We evaluate several lexicon-based and corpus-based methods to automatically induce new lexical units for Swedish FrameNet, and we see that the best-performing setup uses a combination of both types of methods. A particular challenge for Swedish is the absence of a lexical resource such as WordNet; however, we show that the semantic network Saldo, which is organized according to lexicographical principles quite different from those of WordNet, is very useful for our purposes.
Article outline
  • 1.Introduction
    • 1.1Frame semantics and frame-semantic lexicons
  • 2.Computational representation of the meaning of words
    • 2.1The semantic network Saldo
    • 2.2Semantic representations induced from corpora
      • 2.2.1Word representations from a class-based n-gram model
      • 2.2.2Geometric word representations from co-occurrences
      • 2.2.3Geometric representations from contextual classifiers
  • 3.From word meaning to frame meaning
    • 3.1Methods based on distance and similarity measures
    • 3.2Classification-based methods
      • 3.2.1Representing the meaning of a word using Saldo
  • 4.Quantitative evaluation
    • 4.1Evaluation metrics
    • 4.2Which way is the best to make use of the Saldo lexicon?
    • 4.3Which corpus-based semantic representations are most effective?
    • 4.4Combining lexicon-based and corpus-based classifiers
    • 4.5For which frames are our methods successful?
    • 4.6Use by lexicographers
  • 5.Conclusion
  • Acknowledgements
  • Notes
  • References
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