Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation

Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic representations in humans by making it possible to directly observe brain activity while people comprehend words and sentences. In this study, we investigate how humans comprehend adjective-noun phrases (e.g. strong dog) while their neural activity is recorded. Classification analysis shows that the distributed pattern of neural activity contains sufficient signal to decode differences among phrases. Furthermore, vector-based semantic models can explain a significant portion of systematic variance in the observed neural activity. Multiplicative composition models of the two-word phrase outperform additive models, consistent with the assumption that people use adjectives to modify the meaning of the noun, rather than conjoining the meaning of the adjective and noun.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Kai-min K. Chang, Vladimir Cherkassky, Tom M. Mitchell, Marcel Adam Just
• Keywords: ACL 2009, Computational Linguistics, Functional Magnetic Resonance Imaging, Neural Activity, Observed Neural Activity