Computational historical linguistics
Table 4: Automatic cognate clustering for concept “person”
['doculect', 'word', 'class label']
For each concept, a weighted graph is constructed, with the words denoting this concept as vertices. Two vertices are connected if the predicted probability of these words to be synonymous (based on their PMI similarity and the logistic regression model) is ≥0.5. The weight of each edge equals the predicted probabilities. The nodes of the graph are clustered using the weighted version of the Label Propagation algorithm ( Raghavan \BOthers. (Csardi \BBA As a result, a class label is assigned to each word. Non-synonymous words never carry the same class label. Both types of characters carry a diachronic signal. For instance, the mutation 0→1 for class label 6/concept person (cf. The mutation 0→1 for the soundclass-concept character k/person represents the same historical process. Soundclass-concept characters, however, also capture sound shifts. For instance, the mutation 0→1 for b/person reflects the epenthetic insertion of b in descendants of Latin “homo” in some Iberian dialects. The pie charts at the nodes display the probability distribution for that node, where white represents 0 and red 1.