SEMANTIC AMBIGUITY PROCESSING IN HUMAN COGNITION: A COMPARATIVE ANALYSIS OF NLP MODELS AND PSYCHOLINGUISTIC APPROACHES
Keywords:
Semantic ambiguity, human cognition, NLP models, psycholinguistics, language processing, context, disambiguation, constraint-based models, modularity, incremental processing.Abstract
Semantic ambiguity is a pervasive phenomenon in both natural language use and computational language processing. This paper investigates how semantic ambiguities are processed in human cognition from a psycholinguistic perspective and compares these processes to the mechanisms employed by modern Natural Language Processing (NLP) models. Drawing on insights from cognitive science, linguistics, and artificial intelligence, we explore how humans resolve ambiguity dynamically using context, prior knowledge, and cognitive heuristics, while machine learning models rely primarily on statistical patterns and pre-trained embeddings. We also discuss theoretical frameworks such as constraint-based models and modularity of mind to better understand the differences between human and machine ambiguity resolution. Finally, we consider how integrating psycholinguistic theories into NLP design could advance the development of more cognitively plausible language technologies.
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