Ekstrakcija metapodatkov s pomočjo strojnega učenja - Sabadin, Ivančica (Moderna arhivistika, 2023)
The aim of this paper is to explore metadata extraction techniques using machine learning. The method used was a literature review of the ProQuest, Scopus and Emerald Insight databases. The results showed that machine learning techniques are already well established in the field of metadata extraction from scientific literature. The best results were shown by solutions that combined document layout analysis and metadata extraction processes. Based on the research on metadata extraction using machine learning, it can be concluded that further analysis of the machine learning tools and models GROBID, CERMINE, XTRACT, BERT, Mask R-CNN and BiLSTM is needed. Based on the experience with metadata extraction from scientific literature, the models should be adapted for metadata extraction from archival material.
Keywords: ekstrakcija metapodatkov; machine learning; machine learning model; metadata extraction; modeli strojnega učenja; nadzorovano učenje; natural language processing; obdelava naravnega jezika; strojno učenje; supervised learning models