Difference between revisions of "Using Wikipedia to Learn Semantic Feature Representations of Concrete Concepts in Neuroimaging Experiments"
(Starting an article - Using Wikipedia to Learn Semantic Feature Representations of Concrete Concepts in Neuroimaging Experiments) |
(Adding wikilinks) |
||
| Line 1: | Line 1: | ||
| − | '''Using Wikipedia to Learn Semantic Feature Representations of Concrete Concepts in Neuroimaging Experiments''' - scientific work related to Wikipedia quality published in 2013, written by Francisco Pereira, Matthew Botvinick and Greg Detre. | + | '''Using Wikipedia to Learn Semantic Feature Representations of Concrete Concepts in Neuroimaging Experiments''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Francisco Pereira]], [[Matthew Botvinick]] and [[Greg Detre]]. |
== Overview == | == Overview == | ||
| − | In this paper authors 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. Authors use topic models on 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. Authors 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. | + | In this paper authors 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. Authors use topic models on 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. Authors 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. |
Revision as of 09:57, 30 September 2019
Using Wikipedia to Learn Semantic Feature Representations of Concrete Concepts in Neuroimaging Experiments - scientific work related to Wikipedia quality published in 2013, written by Francisco Pereira, Matthew Botvinick and Greg Detre.
Overview
In this paper authors 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. Authors use topic models on 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. Authors 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.
