Abstract
We present muppets, a framework for partitioning cells in a table in segments that fulfil the same semantic role or belong to the same semantic data type, similar to how image segmentation is used to group pixels that represent the same semantic object in computer vision. Flexible constraints can be imposed on these segmentations for different use cases. muppets uses a hierarchical merge tree algorithm, which allows for efficiently finding segmentations that satisfy given constraints and only requires similarities between neighbouring cells to be computed. Three applications are used to illustrate and evaluate muppets: identifying tables and headers, type detection and discovering semantic errors.
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Acknowledgements
This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No [694980] SYNTH: Synthesising Inductive Data Models). This research received funding from the Flemish Government (AI Research Program), the EU (FEDER) and the Spanish MINECO RTI2018-094403-B-C32 and the Generalitat Valenciana PROMETEO/2019/098. LCO was also supported by the Spanish MECD grant (FPU15/03219).
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Verbruggen, G., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Raedt, L.D. (2021). Muppets: Multipurpose Table Segmentation. In: Abreu, P.H., Rodrigues, P.P., Fernández, A., Gama, J. (eds) Advances in Intelligent Data Analysis XIX. IDA 2021. Lecture Notes in Computer Science(), vol 12695. Springer, Cham. https://doi.org/10.1007/978-3-030-74251-5_31
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