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. 2011;6(8):e23912.
doi: 10.1371/journal.pone.0023912. Epub 2011 Aug 24.

Global and local features of semantic networks: evidence from the Hebrew mental lexicon

Yoed N Kenett  1 , Dror Y Kenett, Eshel Ben-Jacob, Miriam Faust
Affiliations

Global and local features of semantic networks: evidence from the Hebrew mental lexicon

Yoed N Kenett et al. PLoS One. 2011.

Abstract

Background: Semantic memory has generated much research. As such, the majority of investigations have focused on the English language, and much less on other languages, such as Hebrew. Furthermore, little research has been done on search processes within the semantic network, even though they are abundant within cognitive semantic phenomena.

Methodology/principal findings: We examine a unique dataset of free association norms to a set of target words and make use of correlation and network theory methodologies to investigate the global and local features of the Hebrew lexicon. The global features of the lexicon are investigated through the use of association correlations--correlations between target words, based on their association responses similarity; the local features of the lexicon are investigated through the use of association dependencies--the influence words have in the network on other words.

Conclusions/significance: Our investigation uncovered Small-World Network features of the Hebrew lexicon, specifically a high clustering coefficient and a scale-free distribution, and provides means to examine how words group together into semantically related 'free categories'. Our novel approach enables us to identify how words facilitate or inhibit the spread of activation within the network, and how these words influence each other. We discuss how these properties relate to classical research on spreading activation and suggest that these properties influence cognitive semantic search processes. A semantic search task, the Remote Association Test is discussed in light of our findings.

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Conflict of interest statement

Competing Interests: Eshel Ben-Jacob is a PLoS ONE Academic Editor. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Association Histogram.
Histogram of the number of association responses to target words.
Figure 2
Figure 2. Association correlation matrix.
The dendrogram hierarchal clustering method is used to find cliques of words with a strong semantic similarity (left panel), and then to order the normalized association correlation matrix (right panel).
Figure 3
Figure 3. Degree distribution plot.
Plot of degree distribution of target words in the correlation network, in a log-log scale.
Figure 4
Figure 4. Network 2D visualization.
Representation of the entire network of 800 words, as they are grouped together in the planar graph, constructed from the association correlations. Each word is a node in the network (green circle), and a link between two words represents their association correlation (blue line).
Figure 5
Figure 5. The making bread clique.
An example of a clique from the full network, semantically concentrated on the notion of making bread.
Figure 6
Figure 6. The outdoor cliques.
An example of three cliques from the full network, semantically concentrated on foot, sky and hiking. The three cliques are related in their semantic focus, with the left centered on the notion of feet, and the right bottom centered on the notion of the sky, and the top right centered on the notion of hiking.
Figure 7
Figure 7. An example of “Gateway nodes”.
The cliques presented in Figure 6, concerning the notion of foot, hiking and sky, are connected by two “gateway nodes” – barefoot (‘yachef’) and sunset (shkia’).
Figure 8
Figure 8. Impact score of the network.
The impact of a given word i on the semantic network, calculated as the difference between the average shortest path of the full network to that of the network after deletion of the word i.
Figure 9
Figure 9. Association dependency network.
A 2D visualization of the full association dependency network (left panel), and an example of a dependency clique in the network, showing association dependencies and related to the notion of making bread (right panel).
Figure 10
Figure 10. OutDegree and InDegree distributions.
OutDegree (left panel) and inDegree distribution (right panel) of node dependency. The outDegree refers to how many nodes are influenced by node i, whereas the inDegree refers to how many nodes influence node i. The x-label outDegree (or inDegree)refers to to the outDegree (or inDegree) score and the y-label frequency refers to the amount of nodes with that outDegree (or inDegree) score.
Figure 11
Figure 11. Relative influence score characterization.
Percentage of different types of nodes, based on their relative influence score – influence nodes are nodes who have an outDegree > 1 and inDegree = 0; receiver nodes are nodes who have an outDegree = 0 and inDegree > 1; zero nodes are nodes who have an outDegree = inDegree; negative nodes are nodes who have an outDegree < inDegree; and positive nodes are nodes who have an outDegree > inDegree.
Figure 12
Figure 12. Top 10 strongest nodes based on their outDegree scores.
X axis represents the nodes and Y axis represents the outDegree score. Highlighted in orange are nodes which are influence nodes, as described above.
See this image and copyright information in PMC

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