Using Natural Language Processing in the Field of Occupations, Qualifications and Skills

On December 13, 2019, the Educational Research Institute organized a seminar with stakeholders of the qualifications system and practitioners in linguistic engineering.

The seminar was organised to discuss the progress, needs and opportunities relating to using text processing algorithms for developing analytics and applications for lifelong learning.

The seminar began with context presentations, giving all participants the opportunity to learn about the European Commission’s plans for ESCO development, the applications and tools developed in the Polish Qualifications Register project and to gain a general overview of the research conducted in the field of qualifications and occupations.

The time set aside for discussions was used to its fullest. The presentation of Textkernel by Bauke Visser spurred numerous questions about the representation of skills, data mining, curating the skills dataset as well as fuelled considerations on developing empirical-based taxonomies for skills and knowledge. The discussion ended with tests of the 3D visualisation of Skills Explorer using goggles.

The case of the relatedness of qualifications in the Polish register, represented inter alia by dendrograms and a relational database (Neo4j) was also widely discussed. After-lunch table discussions touched on potential opportunities, but also on the quality and representativeness of available data, which can be matched with qualifications – learning materials (video, text), descriptions of occupations, etc.

The meeting concluded with declarations of interest in follow-up meetings, with an equal amount of time to be reserved for individual and group interactions.

DOWNLOADS

  1. ESCO Recent developments & results of skills qualifications mapping
  2. European research in the field of occupations, qualifications and skills information about selected initiatives
  3. Polish Qualifications Register
  4. Clustering and Visualisation of Qualifications Preliminary Results
  5. Textkernel Skill Explorer

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