Abstract
The automatic extraction of formal process information from textual descriptions of processes is a challenging problem, but worth exploring, since it enables organizations to align complementary information that talks about processes. In this paper we continue our previous work on this area, based on defining hierarchical/tree patterns on the dependency trees that arise from the linguistic analysis. We now incorporate a new abstraction layer on these patterns, that consider relationships between nearby sentences. The aim of this extension is to capture inter-sentence relationships that typically arise in textual descriptions of processes. The experiments done on publicly available benchmarks corroborate this intuition, showing a significant rise in the ability to capture all the important control-flow relationships defined in the text.
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Notes
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The reader can see a tutorial for annotating process modeling exercises in the ModelJudge platform at https://modeljudge.cs.upc.edu/modeljudge_tutorial/.
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In this work we consider a flattened version of the ATDP language, i.e., without the notion of scopes.
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Acknowledgments
This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R, and by the Ecuadorian National Secretary of Higher Education, Science and Technology (SENESCYT).
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Quishpi, L., Carmona, J., Padró, L. (2021). Improving the Extraction of Process Annotations from Text with Inter-sentence Analysis. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_12
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