Context Independent

Context-independent representations in machine learning aim to create features or embeddings that capture the inherent meaning of data elements without relying on surrounding context. Current research focuses on improving the accuracy and efficiency of these representations, particularly within natural language processing (NLP) tasks like emotion recognition and clinical outcome detection, often leveraging transformer-based models like BERT and RoBERTa. While some studies suggest context-independent approaches can be effective in specific scenarios, others highlight their limitations when dealing with the inherent ambiguity and context-dependency of natural language. The development of robust context-independent representations is crucial for improving the generalizability and efficiency of various machine learning applications.