Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges

2 code implementations • 7 Oct 2021 • Patrizio Bellan, Mauro Dragoni, Chiara Ghidini, Han van der Aa, Simone Paolo Ponzetto

The extraction of process models from text refers to the problem of turning the information contained in an unstructured textual process descriptions into a formal representation, i. e., a process model.

Benchmarking Model extraction +1

PET: An Annotated Dataset for Process Extraction from Natural Language Text

no code implementations • 9 Mar 2022 • Patrizio Bellan, Han van der Aa, Mauro Dragoni, Chiara Ghidini, Simone Paolo Ponzetto

Therefore, to bridge this gap, we present the PET dataset, a first corpus of business process descriptions annotated with activities, gateways, actors, and flow information.

Leveraging pre-trained language models for conversational information seeking from text

no code implementations • 31 Mar 2022 • Patrizio Bellan, Mauro Dragoni, Chiara Ghidini

Recent advances in Natural Language Processing, and in particular on the construction of very large pre-trained language representation models, is opening up new perspectives on the construction of conversational information seeking (CIS) systems.

Few-Shot Learning In-Context Learning