Abstract
We describe the process metadata of GRAIN, a complex language data corpus, as a show case for application of metadata in the Digital Humanities. While the creation of language resources usually involves some automatic processing ranging from format conversion to labeling of structural features, data selection, inspection and interpretation are important manual steps, which tend to be neglected in the description of scientific workflows. GRAIN makes use of a format which (i) maps all workflow steps to flexible triples of \(\{input,operator,output\}\) and (ii) treats manual and automatic steps equally. Moreover, the process metadata has been semi-automatically generated and allows for a straightforward visualization describing the creation of the resource.
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Notes
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Cf. [2] for the impact of a minor decision in the data creation process on several downstream tasks.
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The definition of ‘word’ is highly discussed among linguists, this contribution thus opts for a colloquial understanding of the term.
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About 140 interviews of 9–10 minutes each with up to 24 annotation layers.
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The manual syntactic annotation is not in the GRAIN release, but work in progress.
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In the creation of GRAIN, git (https://git-scm.com/) was applied.
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About 6350 JSON structures with process metadata for the automatic part.
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Jung, K., Gärtner, M., Kuhn, J. (2019). Fine-GRAINed Process Metadata. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_33
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