Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language
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.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
