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Neural Correlates of Concreteness in Semantic Categorization
Journal of Cognitive Neuroscience, 2007Abstract In some contexts, concrete words (CARROT) are recognized and remembered more readily than abstract words (TRUTH). This concreteness effect has historically been explained by two theories of semantic representation: dual-coding [Paivio, A. Dual coding theory: Retrospect and current status.
Penny M. Pexman +4 more
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Attribute Grammars and Categorical Semantics
2008We give a new formulation of attribute grammars (AG for short) called monoidalAGsin traced symmetric monoidal categories. Monoidal AGs subsume existing domain-theoretic, graph-theoretic and relational formulations of AGs. Using a 2-categorical aspect of monoidal AGs, we also show that every monoidal AG is equivalent to a synthesised one when the ...
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Semantic adaptive microaggregation of categorical microdata
Computers & Security, 2012In the context of Statistical Disclosure Control, microaggregation is a privacy-preserving method aimed to mask sensitive microdata prior to publication. It iteratively creates clusters of, at least, k elements, and replaces them by their prototype so that they become k-indistinguishable (anonymous).
Sergio Martínez +2 more
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Semantically Modeling of Object and Context for Categorization
IEEE Transactions on Neural Networks and Learning Systems, 2019Object-centric-based categorization methods have been proven more effective than hard partitions of images (e.g., spatial pyramid matching). However, how to determine the locations of objects is still an open problem. Besides, modeling of context areas is often mixed with the background.
Chunjie Zhang 0001 +2 more
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Semantic search and semantic categorization (abstracts)
ACM SIGIR Forum, 1997Hsinchun Chen +3 more
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SemCat: semantically categorized entities for genomics.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2007We describe the construction of a semantic database called SemCat consisting of a large number of semantically categorized names relevant to genomics. SemCat can be used to facilitate natural language processing in MEDLINE. We present suitable application areas including biomedical name classification and named entity recognition.
Lorraine K. Tanabe +4 more
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Some Thoughts on Health Surveillance Data, Race, and Population Categorization
Ca-A Cancer Journal for Clinicians, 2016Otis Bralwey
exaly
A Discriminative and Semantic Feature Selection Method for Text Categorization
International Journal of Production Economics, 2015Wei Zong
exaly