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Global FrameNet
  • Home/
  • Partners/
  • Annotation/
  • Repositories/
  • Learning Center/
    • ISFS2012
  • Events/
    • PAC 2025
    • IFNW 2025
    • IFNW 2023
    • Global FrameNet Tuesdays
    • Constructicon Alignment Workshop 2022
    • Construction Annotation Workshop 2020
    • IFNW 2020
    • IFNW 2018
    • Multilingual FrameNet Workshop 2018
    • Multilingual FrameNet Meeting 2017
    • IFNW 2016
    • IFNW 2013
Global FrameNet

the hub for collaboration in the development of frame-based language resources and applications

Loja

Global FrameNet
  • Home/
  • Partners/
  • Annotation/
  • Repositories/
  • Learning Center/
    • ISFS2012
  • Events/
    • PAC 2025
    • IFNW 2025
    • IFNW 2023
    • Global FrameNet Tuesdays
    • Constructicon Alignment Workshop 2022
    • Construction Annotation Workshop 2020
    • IFNW 2020
    • IFNW 2018
    • Multilingual FrameNet Workshop 2018
    • Multilingual FrameNet Meeting 2017
    • IFNW 2016
    • IFNW 2013
  • Home/
  • Partners/
  • Annotation/
  • Repositories/
  • Learning Center/
    • ISFS2012
  • Events/
    • PAC 2025
    • IFNW 2025
    • IFNW 2023
    • Global FrameNet Tuesdays
    • Constructicon Alignment Workshop 2022
    • Construction Annotation Workshop 2020
    • IFNW 2020
    • IFNW 2018
    • Multilingual FrameNet Workshop 2018
    • Multilingual FrameNet Meeting 2017
    • IFNW 2016
    • IFNW 2013

Global FrameNet

Shared Annotation Task

The shared annotation task was devised in part as a means to evaluate the complexity of the work required to align the FrameNets developed for different languages during the past decade and more. By annotating either translations of a given text or comparable texts from the same genre and on the same topic, we aim to assess what kinds of differences must exist between FrameNets for different languages in order to provide an adequate analysis of the lexicon of each language. Moreover, the shared annotation task will generate a collection of texts annotated with frames and LUs for several languages, which can be used in the future, for instance, as training data in a variety of applications.

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