Non Linguistic

Non-linguistic research explores how computational methods can analyze and leverage information beyond traditional text, encompassing diverse data types like chess game notations, financial records, and even visual elements from book covers. Current research focuses on applying techniques like word embeddings, large language model (LLM) embeddings, and neural network architectures to extract meaningful representations from these non-semantic data, often for tasks such as anomaly detection or improved natural language generation. This field is significant because it expands the capabilities of machine learning beyond linguistic data, enabling more robust and comprehensive analyses across various domains and potentially leading to advancements in areas like cybersecurity and financial fraud detection.