S. Ağan

A Teacher-Student BERT Architecture for Semi-Supervised Learning on Unstructured Social Work Texts: Identifying Service Gaps and Needs

To address the “information gap” in social work arising from unstructured data and limited labeled instances, this study proposes a semi-supervised learning framework based on a Teacher-Student BERT architecture. Based on an analysis of 50,000 case records spanning 2019 to 2024, the model incorporates Latent Dirichlet...