We address these problems by including geographic information via retrofitting (Faruqui et al., 2015;Hovy and Fornaciari, 2018): we use administrative region boundaries to modify the city embeddings, and evaluate the resulting vectors in a clustering approach to discover larger dialect regions.
to most dialectometric approaches (Nerbonne et al., 1999;Prokić and Nerbonne, 2008), and in line with common NLP practice (Doyle, 2014;Grieve, 2016;Huang et al., 2016;Rahimi et al., 2017a), we also evaluate the clustered dialect areas quantitatively.