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 Representation (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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In Proceedings of the 6th Workshop on Cognitive Aspects of the Lexicon (CogALex-VI), Barcelona, ES, December 2020.

@inproceedings{aguirre-celis:cogalex20, title={Characterizing Dynamic Word Meaning Representations in the Brain}, author={Nora Aguirre-Celis and Risto Miikkulainen}, booktitle={Proceedings of the 6th Workshop on Cognitive Aspects of the Lexicon (CogALex-VI)}, month={December}, address={Barcelona, ES}, url="http://www.cs.utexas.edu/users/ai-lab?aguirre-celis:cogalex20", year={2020} }