Tec-Habilidad: Skill Classification for Bridging Education and Employment

Job application and assessment processes have evolved significantly in recent years, largely due to advancements in technology and changes in the way companies operate. Skill extraction and classification remain an important component of the modern hiring process, as they provide a more objective way to evaluate candidates and automatically align their skills with the job requirements. However, to effectively evaluate the skills, the skill extraction tools must recognize varied mentions of skills on resumes, including direct mentions, implications, synonyms, acronyms, phrases, and proficiency levels, and differentiate between hard and soft skills. While tools like LLMs (Large Language Models) help extract and categorize skills from job applications, there¿s a lack of comprehensive datasets for evaluating the effectiveness of these models in accurately identifying and classifying skills in Spanish-language job applications. This gap hinders our ability to assess the reliability and precision of the models, which is crucial for ensuring that the selected candidates truly possess the required skills for the job. In this paper, we develop a Spanish language dataset for skill extraction and classification, provide an annotation methodology to distinguish between knowledge, skill, and abilities, and provide deep learning baselines to advance robust solutions for skill classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Tec-Habilidad: Skill Classification for Bridging Education and Employment Chapter in Scopus