Automatic Extraction of Lithuanian Cybersecurity Terms Using Deep Learning Approaches
Aivaras Rokas, Sigita Rackevičienė, Andrius Utka · Frontiers in artificial intelligence and applications · 2020
The paper presents the results of research on deep learning methods aiming to determine the most effective one for automatic extraction of Lithuanian terms from a specialized domain (cybersecurity) with very restricted resources. A semi-supervised approach to deep learning was chosen for the research as Lithuanian is a less resourced language and large amounts of data, necessary for unsupervised methods, are not available in the selected domain. The findings of the research show that Bi-LSTM network with Bidirectional Encoder Representations from Transformers (BERT) can achieve close to state-of-the-art results.