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.
Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions.
and (2) How can we identify for a new user the best utility function from amongst those that we have learned?
no code implementations • 13 Nov 2021 • Adrien Bennetot, Ivan Donadello, Ayoub El Qadi, Mauro Dragoni, Thomas Frossard, Benedikt Wagner, Anna Saranti, Silvia Tulli, Maria Trocan, Raja Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia Díaz-Rodríguez
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
Automatic Process Discovery aims at developing algorithmic methodologies for the extraction and elicitation of process models as described in data.
In the last decade, the need of having effective and useful tools for the creation and the management of linguistic resources significantly increased.