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Title: Deep dive data and knowledge
Other Titles: European language equality
Authors: Kaltenböck, Martin
Revenko, Artem
Choukri, Khalid
Boytcheva, Svetla
Lieske, Christian
Lynn, Teresa
Rigau, German
Heuschkel, Maria
Farwell, Aritz
Jones, Gareth
Aldabe, Itziar
Estarrona, Ainara
Marheinecke, Katrin
Piperidis, Stelios
Arranz, Victoria
Vandeghinste, Vincent
Borg, Claudia
Keywords: Artificial intelligence
Translating and interpreting -- Technological innovations
Machine learning
Translating machines
Multilingualism -- European Union countries
Issue Date: 2023
Publisher: Springer
Citation: Kaltenböck, M., Revenko, A., Choukri, K., Boytcheva, S., Lieske, C., Lynn, T.,…Borg, C. (2023). Deep Dive Data and Knowledge. In G. Rehm & A. Way (Eds.), European Language Equality (pp. 337-359). Cham: Springer.
Abstract: This deep dive on data, knowledge graphs (KGs) and language resources (LRs) is the final of the four technology deep dives, as data as well as related models are the basis for technologies and solutions in the area of Language Technology (LT) for European digital language equality (DLE). This chapter focuses on the data and LRs required to achieve full DLE in Europe by 2030. The main components identified – data, KGs, LRs – are explained, and used to analyse the state-of-the-art as well as identify gaps. All of these components need to be tackled in the future, for the widest range of languages possible, from official EU languages to dialects to non- EU languages used in Europe. For all these languages, efficient data collection and sustainable data provision to be facilitated with fair conditions and costs. Specific technologies, methodologies and tools have been identified to enable the implementation of the vision of DLE by 2030. In addition, data-related business models and data-governance models are discussed, as they are considered a prerequisite for a working data economy that stimulates a vibrant LT landscape that can bring about European DLE.
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