Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Improvements in automatic thesaurus extraction
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Cognitively plausible models of human language processing
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Research on Language and Computation
Learning semantic features for fMRI data from definitional text
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Detecting semantic category in simultaneous EEG/MEG recordings
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Comparing EEG/ERP-like and fMRI-like techniques for reading machine thoughts
BI'10 Proceedings of the 2010 international conference on Brain informatics
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
Parallels between machine and brain decoding
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Hi-index | 0.00 |
Mitchell et al. (2008) demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI. This could be a very powerful technique for evaluating conceptual models extracted from corpora; however, fMRI is expensive and imposes strong constraints on data collection. Following on experiments that demonstrated that EEG activation patterns encode enough information to discriminate broad conceptual categories, we show that corpus-based semantic representations can predict EEG activation patterns with significant accuracy, and we evaluate the relative performance of different corpus-models on this task.