Abstract
Business process management encompasses a variety of tasks that can be solved system-aided but usually require formal process representations, i.e. process models. However, it requires a significant effort to learn a formal process modeling language like, for instance, BPMN. Among others, this is one reason why companies often still stick to informal textual process descriptions. However, in contrast to formal models, information from natural language text usually cannot be automatically processed by algorithms. Hence, recent research also focuses on annotated textual process descriptions to make text machine processable.
While still human-readable, they additionally contain annotations following a formal scheme. Thus, they also enable automated processing by, for instance, formal reasoning and simulation. State-of-the-art techniques for automatically annotating textual process descriptions are either based on hand-crafted rule sets or artificial neural networks. Maintaining complex rule sets requires a significant manual effort and the approaches using neural networks suffer from rather low result quality. In this paper we present an approach based on Semantic Parsing and Graph Convolutional Networks that avoids manually defined rules and provides significantly better results than existing techniques based on neural networks. A comprehensive evaluation using multiple data sets from both academia and industry shows encouraging results and differentiates between several applied text features.
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Notes
- 1.
Literature refers to Clause Classification and Clause Semantics Recognition as Sentence Classification and Sentence Semantics Recognition, which suggests processing of whole sentences, though the discussed approach operates on clauses instead.
- 2.
Our code can be accessed at https://github.com/JulianNeuberger/UCCA4BPM.
- 3.
see https://universaldependencies.org/u/pos/, accessed 2020/12/5.
- 4.
Using token based node features, inner nodes use the zero vector as feature, since they do not have a corresponding token. Therefore, two edges need to be traversed before the incoming edge information is aggregated in a terminal node: The artificial inverse edge “up” the UCCA structure and only then the edge in question, see Sect. 4.
- 5.
https://universaldependencies.org/u/feat/index.html, accessed 2020/12/5.
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Acknowledgements
We thank Omri Abend (HUJI) and Daniel Hershcovich (UCPH) for their assistance with UCCA, Lluís Padró, Luis Quishpi and Josep Carmona (UPC) for valuable advice regarding their approach, and the DBIS Chair (UBT) for assistance creating the new dataset.
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Ackermann, L., Neuberger, J., Jablonski, S. (2021). Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_5
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