We present a series of methods for deriving conceptual representations from corpora and investigate the usefulness of the fMRI data and machine learning methodology of Mitchell et al. (2008) as a basis for evaluating the different models. Within this framework, the quality of a semantic model is quantified by its ability to predict the fMRI activation associated with conceptual stimuli. Mitchell et al. used a manually-acquired set of verbs as the basis for their semantic model; in this paper, we also consider automatically acquired feature-norm-like semantic representations. These models make different assumptions about the kinds of information available in corpora that is relevant to representing conceptual knowledge. Our results indicate that automatically-acquired representations can make equally powerful predictions about the brain activity associated with the stimuli.
Devereux et al REF applied the framework to evaluate four different feature extraction methods, each based on a different source of information available in corpora.
Using fMRI activation to conceptual stimuli to evaluate methods for extracting conceptual representations from corpora
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Computer Science