GraphColl: A Methodology for Visualization and Quantification of Semantic Networks in English

The aim of this paper is to illustrate a methodology used to construct a radial network based on family resemblances or on semantic components that allows one to visualize and measure the relationship between a specific set of words through the use of GraphColl (Brezina et al. 2015). Bagli (2018) successfully used it to visualize the interrelated network of categorial associations in the domain of Taste with the participation of Native Speakers; while Sandford used the tool to visualize the semantic relationships that hold between a set of Manner Of Speaking Verbs in English. In both cases, the authors uploaded the informant responses in a .txt file to the collocation software GraphColl, and then verified the correlation strength of the judged components by considering Mutual Information as a statistical measure. The results illustrated through the software reveal a visually communicative graph that allows us to understand the correlations that are also fully verifiable through various statistical measures.