SciBERT Embeddings

SciBERT embeddings, derived from a pre-trained language model specifically trained on scientific text, are increasingly used to improve various natural language processing tasks within the scientific domain. Current research focuses on leveraging SciBERT embeddings in conjunction with other architectures, such as CNNs and GPT-2, to enhance tasks like multi-label classification of scientific literature, automatic generation of figure captions and research highlights, and hierarchical patent classification. These advancements aim to improve the efficiency and effectiveness of scientific information processing, facilitating literature reviews, knowledge discovery, and the organization of scientific data.