The Journal of Machine Learning Research
Applied morphological processing of English
Natural Language Engineering
EEG responds to conceptual stimuli and corpus semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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(Mitchell et al., 2008) showed that it was possible to use a text corpus to learn the value of hypothesized semantic features characterizing the meaning of a concrete noun. The authors also demonstrated that those features could be used to decompose the spatial pattern of fMRI-measured brain activation in response to a stimulus containing that noun and a picture of it. In this paper we introduce a method for learning such semantic features automatically from a text corpus, without needing to hypothesize them or provide any proxies for their presence on the text. We show that those features are effective in a more demanding classification task than that in (Mitchell et al., 2008) and describe their qualitative relationship to the features proposed in that paper.