Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language. (arXiv:2211.05035v1 [cs.CL])

Word embeddings play a significant role in today's Natural Language
Processing tasks and applications. While pre-trained models may be directly
employed and integrated into existing pipelines, they are often fine-tuned to
better fit with specific languages or domains. In this paper, we attempt to
improve available embeddings in the uncovered niche of the Italian medical
domain through the combination of Contrastive Learning (CL) and Knowledge Graph
Embedding (KGE). The main objective is to improve the accuracy of semantic
similarity between …

arxiv graph italian knowledge knowledge graph language medical word embeddings