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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. 281–302

Chapter 11
Computational representation of FrameNet for multilingual natural language generation

| University of Gothenburg
| IMCS, University of Latvia

Multilingual natural language generation, the process of producing written or spoken utterances in parallel languages from either structured or unstructured representations requires large amounts of syntactic and semantic information to generate an expression that is tailored to the target audience. This information is offered by FrameNet-like resources, which have been developed for a number of languages. In this chapter, we present a computational FrameNet grammar resource for multilingual natural language generation. We compare between English and Swedish framenets to illustrate how these can be unified under a shared computational representation using Grammatical Framework. We demonstrate how the grammar was exploited in two practical multilingual natural language generation applications to facilitate tourist communication and empower museum users with coherent artwork descriptions.

Article outline
  • 1.Introduction
  • 2.Comparison of selected framenets
    • 2.1Berkeley FrameNet
    • 2.2Swedish FrameNet
    • 2.3Summary of the comparison
  • 3.Computational framenets in Grammatical Framework
    • 3.1Grammatical Framework
    • 3.2FrameNet grammar library in GF
    • 3.3Status of the FrameNet grammar library
  • 4.FrameNet-based multilingual NLG
    • 4.1Accurate generation of tourist phrases
    • 4.2Coherent text generation of museum objects
  • 5.Final words
  • Notes
  • References
  • Appendix
Available under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 26 November 2021
DOI logo
https://doi.org/10.1075/nlp.14.11dan
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