Multiple Meaning

Multiple meaning, a central challenge in fields ranging from natural language processing to robotics, focuses on how meaning is represented, interpreted, and used in complex systems. Current research emphasizes developing methods to quantify and model meaning, often employing techniques like contextualized word embeddings (BERT), counterfactual generation, and various machine learning architectures (e.g., transformers, convolutional neural networks) to analyze and improve the handling of ambiguity and context-dependent interpretations. Understanding and effectively managing multiple meanings is crucial for building reliable and trustworthy AI systems, improving human-robot interaction, and advancing our understanding of human cognition and communication.