Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.
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We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain.Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created.This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.
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Affiliation: Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.
ABSTRACT
In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations. No MeSH data available. Related in: MedlinePlus |
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Mentions: To give the co-occurrence adjustment, it is known that the Jaccard similarity can be intuitively formulated as/A∩B//A∪B/(1)for two sets A and B. Indeed, for two vertices, this index is usually computed as/N(a)∩N(b)//N(a)∪N(b)/,(1)'where N(a) denotes the set of all neighbours of vertex a. To enhance the accuracy with which the distance between remote nodes is evaluated, we extend the interpretation of expression (1) such that the numerator is the distance of the shortest path connecting vertices a and b. The denominator in (1) is the sum of the degrees of vertices a and b, or, in some cases, all of the steps starting from these vertices that have an identical step length. In this article, we adopt the latter definition for the denominator, and set the step length equal to the shortest path between a and b in the numerator. Fig 1 illustrates this coefficient using the friendship network of Zachary’s famous “Karate Club” [44]. |
View Article: PubMed Central - PubMed
Affiliation: Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.
No MeSH data available.