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Schulz, Claudia; Sailer, Michael; Kiesewetter, Jan ORCID logoORCID: https://orcid.org/0000-0001-8165-402X; Bauer, Elisabeth ORCID logoORCID: https://orcid.org/0000-0003-4078-0999; Fischer, Frank; Fischer, Martin R. and Gurevych, Iryna (2018): Automatic Recommendations for Data Coding: a use case from medical and teacher education. 14th International Conference on e-Science (e-Science), Amsterdam, Netherlands, 29 Oct.-1 Nov. 2018. IEEE Xplore. IEEE. pp. 364-365

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Research in social sciences and humanities often involves analysing data to draw scientific conclusions. This however requires the manual coding of the data, which is highly time-consuming. A use case is the coding of students' essays in education to draw conclusions about students' reasoning and argumentation. The NeuralWeb API tackles this problem by providing automatic endings to other software components. These codings can for example be used in annotation platforms in terms of reconunendations for expert coders from social sciences and humanities. After sonic initial manual annotations, the expert coders then merely need to verify the correctness of the automatic codings instead of manually annotating all data.

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