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With the ageing global population the demand for nursing jobs and the requirements for complex care provision are increasing. In consequence, nursing professionals need to be ready to adapt, obtain variety of skills and engage in career self-management. The purpose of this paper is to investigate individual, micro-level, resources and behaviors that can facilitate matching processes between nursing professionals and their jobs.

A survey-based study was conducted among 314 part-time and full-time nursing professionals in Hungary.

Consistent with the career construction theory, this study offers evidence on career adaptability as a self-regulatory resource that might stimulate nurses’ adaptation outcomes. Specifically, it demonstrates positive relationships between adaptive readiness (proactive personality and conscientiousness), career adaptability, adapting behaviors (career planning and proactive skill development) and adaptation outcomes (employability and in-role performance).

The cross-sectional design limits causal inference. Relatively small sample of full-time professionals for whom supervisory-ratings were obtained yields the need of further replication.

Stimulating development of nurses’ career adaptability, career planning, and proactive skill development can contribute to sustainable career management. It can facilitate the alignment of nurses to performance requirements of their current jobs, preventing individual person-job mismatch.

Zooming into the context of nursing professionals in Hungary, the study elucidates the understudied link between adaptivity and adapting responses and answers the call for more research that employs other-ratings of adaptation outcomes. It demonstrates the value of career adaptability resources for nurses’ employability and in-role performance.

A hybrid approach is presented, which combines linguistic and statistical information to semi-automatically extract multiword term candidates from texts.

The method is designed to be domain and language independent, focusing on languages with rich morphology. Here, it is used for extracting multiword terms from texts in Serbian, belonging to the agricultural engineering domain, as a use case. Predefined syntactic structures were used for multiword terms. For each structure, a finite state transducer was developed, which recognizes text sequences having that structure and outputs the sequence in a normalized form, so that different inflectional forms of the same multiword term can be counted properly. Term candidates were further filtered by their frequencies and evaluated by two domain experts.

By using language resources, such as electronic dictionaries and grammars, 928 multiword terms were extracted out of 1,523 multiword terms that were recognized as candidates from a corpus having 42,260 different simple word forms; 870 of these were new, not already contained in the existing electronic dictionary of compounds for Serbian, and they were used to enrich the dictionary.

The paper presents methodology that can significantly contribute to the development of terminology lexicons in different areas. In this particular use case, some important agricultural engineering concepts were extracted from the text, but this approach could be used for other domains and languages as well.