tailieunhanh - Báo cáo khoa học: "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 (. 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. . | Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation Kai-min K. Chang1 Vladimir L. Cherkassky2 Tom M. Mitchell3 Marcel Adam Just2 Language Technologies Institute1 Center for Cognitive Brain Imaging2 Machine Learning Department3 Carnegie Mellon University Pittsburgh PA 15213 . kkchang Cherkassky just @ Abstract 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 . 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. 1 Introduction How humans represent meanings of individual words and how lexical semantic knowledge is combined to form complex concepts are issues fundamental to the study of human knowledge. There have been a variety of approaches from different scientific communities trying to characterize semantic representations. Linguists have tried to characterize the meaning of a word with feature-based approaches such as semantic roles Kipper et al. 2006 as well as word-relation approaches such as WordNet Miller 1995 . Computational linguists have demonstrated that a word s meaning is captured to some extent by the distribution of words and phrases with which it commonly co-occurs Church Hanks 1990 . Psychologists have