  In today's digital world there is an increasing focus on soft skills. The
reasons are many, however the main ones can be traced down to the increased
complexity of labor market dynamics and the shift towards digitalisation.
Digitalisation has also increased the focus on soft skills, since such
competencies are hardly acquired by Artificial Intelligence Systems. Despite
this growing interest, researchers struggle in accurately defining the soft
skill concept and in creating a complete and shared list of soft skills.
Therefore, the aim of the present paper is the development of an automated tool
capable of extracting soft skills from unstructured texts. Starting from an
initial seed list of soft skills, we automatically collect a set of possible
textual expressions referring to soft skills, thus creating a Soft Skills list.
This has been done by applying Named Entity Recognition (NER) on a corpus of
scientific papers developing a novel approach and a software application able
to perform the automatic extraction of soft skills from text: the SkillNER. We
measured the performance of the tools considering different training models and
validated our approach comparing our list of soft skills with the skills
labelled as transversal in ESCO (European Skills/Competence Qualification and
Occupation). Finally we give a first example of how the SkillNER can be used,
identifying the relationships among ESCO job profiles based on soft skills
shared, and the relationships among soft skills based on job profiles in
common. The final map of soft skills-job profiles may help accademia in
achieving and sharing a clearer definition of what soft skills are and fuel
future quantitative research on the topic.
