Characterizing Dynamic Word Meaning Representations in the Brain
During sentence comprehension, humans adjust word meanings according to the combination of the concepts that occur in the sentence. This paper presents a neural network model called CEREBRA (Context-dEpendent meaning REpresentation in the BRAin) that demonstrates this process based on fMRI sentence patterns and the Concept Attribute Rep-resentation (CAR) theory. In several experiments, CEREBRA is used to quantify conceptual combination effect and demonstrate that it matters to humans. Such context-based representations could be used in future natural language processing systems allowing them to mirror human performance more accurately.
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