WordNet based features for predicting brain activity associated with meanings of nouns

Authors:
Ahmad Babaeian Jelodar;Mehrdad Alizadeh;Shahram Khadivi
Affiliations:
Amirkabir University of Technology, Tehran, Iran;Amirkabir University of Technology, Tehran, Iran;Amirkabir University of Technology, Tehran, Iran
Venue:
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Year:
2010

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Abstract

Different studies have been conducted for predicting human brain activity associated with the semantics of nouns. Corpus based approaches have been used for deriving feature vectors of concrete nouns, to model the brain activity associated with that noun. In this paper a computational model is proposed in which, the feature vectors for each concrete noun is computed by the WordNet similarity of that noun with the 25 sensory-motor verbs suggested by psychologists. The feature vectors are used for training a linear model to predict functional MRI images of the brain associated with nouns. The WordNet extracted features are also combined with corpus based semantic features of the nouns. The combined features give better results in predicting human brain activity related to concrete nouns.