The Journal of Machine Learning Research
Applied morphological processing of English
Natural Language Engineering
CLAWS4: the tagging of the British National Corpus
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Learning to Decode Cognitive States from Brain Images
Machine Learning
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Connecting language to the world
Artificial Intelligence - Special volume on connecting language to the world
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
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Learning semantic features for fMRI data from definitional text
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
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Collaboratively built semi-structured content and Artificial Intelligence: The story so far
Artificial Intelligence
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In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study by Mitchell et al. (2008) [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that these features can outperform those in Mitchell et al. (2008) [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects.