Neurosemantics aims to learn the mapping between concepts and the neural activity which they elicit during neuroimaging experiments. Different approaches have been used to represent individual concepts, but current state-of-the-art techniques require extensive manual intervention to scale to arbitrary words and domains. To overcome this challenge, we initiate a systematic comparison of automatically-derived corpus representations, based on various types of textual co-occurrence. We find that dependency parse-based features are the most effective, achieving accuracies similar to the leading semi-manual approaches and higher than any published for a corpus-based model. We also find that simple word features enriched with directional information provide a close-tooptimal solution at much lower computational cost.
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