Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language
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Abstract (arXiv)
In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes. P2T integrates three AI paradigms: process mining for extracting temporal and structural information from a process, fuzzy linguistic protoforms for modelling uncertain terms, and natural language generation for building the explanations. A real use-case in the cardiology domain is presented, showing the potential of P2T for providing natural language explanations addressed to specialists.
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References · 4
- 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
- 2017Automated Discovery of Process Models from Event Logs: Review and Benchmarkcited by 28
- 2016Fuzzy Sets Across the Natural Language Generation Pipelinecited by 1
- 2017Mining Frequent Patterns in Process Modelscited by 1
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