Semantic Enrichment

Semantic enrichment focuses on enhancing the meaning and context of data representations, improving the performance of various machine learning tasks. Current research emphasizes integrating semantic information from diverse sources, such as pre-trained language models and ontologies, into existing models (e.g., CNNs, Transformers, LSTMs) to improve accuracy and efficiency in applications like speech editing, image segmentation, and knowledge graph construction. This work is significant because it addresses limitations in existing models by providing richer contextual understanding, leading to improved performance in numerous fields, including healthcare, natural language processing, and cybersecurity.