Word association norms, mutual information, and lexicography
Computational Linguistics
WordNet: a lexical database for English
Communications of the ACM
The Journal of Machine Learning Research
Learning to Decode Cognitive States from Brain Images
Machine Learning
Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex
Journal of Cognitive Neuroscience
Domain-Specific Knowledge Systems in the Brain: The Animate-Inanimate Distinction
Journal of Cognitive Neuroscience
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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.