ACM SIGIR Forum
Contextual correlates of synonymy
Communications of the ACM
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
The Journal of Machine Learning Research
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Unsupervised methods for developing taxonomies by combining syntactic and statistical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Improvements in automatic thesaurus extraction
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Automatic evaluation of students' answers using syntactically enhanced LSA
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
An empirical study of required dimensionality for large-scale latent semantic indexing applications
Proceedings of the 17th ACM conference on Information and knowledge management
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
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
WordNet based features for predicting brain activity associated with meanings of nouns
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
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Thought recognition: predicting and decoding brain activity using the zero-shot learning model
Thought recognition: predicting and decoding brain activity using the zero-shot learning model
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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-to-optimal solution at much lower computational cost.