Simple Search Algorithms on Semantic Networks Learned from Language Use
Table 3: The small-world and clustering results for best networks. σ is small-worldness; P and R are average precision and recall, respectively.
['Network', 'Structure  [ITALIC] σ', 'Structure Nodes', 'Structure Edges', 'Semantics P', 'Semantics R', 'Semantics F-score']
As in \shortciteAnematzadeh.etal.2014.cogsci, we calculate a “small-worldness” score (σ) for each of our networks, using well-known graph metrics; when σ>1, the network conforms to a small-world structure. We find that all the networks that exhibit the target IRT pattern have a small-world structure; in other words, a small-world structure may be necessary in producing the human pattern. However, having a small-world structure is not sufficient: most of the networks under the wide range of parameter settings we examined have small-world structure, but not all exhibit the foraging behavior. We observe that an appropriate graph structure on its own cannot guarantee efficient search and retrieval: For that, the content of the sub-networks need to appropriately link semantically-related words. Indeed, \shortciteAabbott.etal.2015 also find that their network captures appropriate groupings of animals. We considered whether our networks also reflect the structure of animal subcategories. For the Learner and Gold networks, we can do this by removing the animal node and its edges (which we added as the cue word for the random walk), and then labeling each connected component of the network with the most frequently occurring category from \citeAtroyer.etal.1997. Although not all subcategories of animals are connected to each other (lower recall), the sub-networks have mostly animals from the same subcategory (high average precision), supporting the observed human-like patch switching.