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.

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

M. Bagli;
2018

Abstract

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.
File in questo prodotto:
File Dimensione Formato  
Sandford_Bagli_GraphColl_Fare scienza oggi_interno-1.pdf

accesso chiuso

Tipologia: Documento in versione editoriale
Dimensione 963.09 kB
Formato Adobe PDF
963.09 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/983333
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact