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This paper introduces a Few-Shot Semantic Meta-Learning framework with CRF (FSM-CRF) for Indonesian skill entity recognition, specifically addressing the challenge of skill extraction in low-resource settings with evolving skill expressions. The FSM-CRF model integrates semantic span representations, episodic meta-learning, and BIO-constrained CRF decoding to improve prototype stability and entity-boundary precision. Experiments on the NERSkill.id dataset demonstrate that FSM-CRF achieves a micro-F1 of 73.84% in a 3-way, 10-shot setting, outperforming both traditional supervised methods and existing few-shot baselines.
Skill extraction in low-resource languages gets a boost: a new meta-learning approach achieves state-of-the-art results with only a handful of examples.
The accelerating pace of digital transformation is reshaping labour-market dynamics, driving the emergence of new competencies, and intensifying the need for scalable skill-intelligence systems within open innovation ecosystems. Yet, research on Indonesian Named Entity Recognition (NER) remains limited for skill-extraction tasks, especially in low-resource contexts where annotated data are scarce and novel skill expressions evolve rapidly. To address this gap, this study contributes to applied Natural Language Processing (NLP) by introducing the Few-Shot Semantic Meta-Learning framework with CRF (FSM-CRF) for Indonesian skill entity recognition, which integrates semantic span representations, episodic meta-learning, and BIO-constrained CRF decoding to enhance prototype stability and entity-boundary precision for complex, multi-token skill expressions. Using the NERSkill.id dataset, the proposed model is evaluated under a 3-way, 10-shot episodic setting and achieves a micro-F1 of 73.84%, outperforming traditional supervised approaches (IndoBERT fine-tuning, BiLSTM-CRF) and existing few-shot baselines. Ablation experiments further demonstrate that semantic span modelling and structured CRF inference play pivotal roles in improving robustness, while meta-learning strengthens adaptability across diverse and evolving skill categories. From an open innovation perspective, this framework offers a data-efficient solution for dynamic competency mapping, reducing dependence on costly annotation pipelines and enabling continuous updates to workforce skill taxonomies. Overall, the findings highlight semantic meta-learning as a promising foundation for next-generation skill-intelligence infrastructures that support AI-enabled innovation management, strategic workforce planning, and evidence-informed policy design.