CLAWS4: the tagging of the British National Corpus
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
The second release of the RASP system
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
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
Acquiring human-like feature-based conceptual representations from corpora
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Acquiring human-like feature-based conceptual representations from corpora
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Selecting corpus-semantic models for neurolinguistic decoding
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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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.