Towards a G reek Proverb Atlas: Computational Spatial Exploration and Attribution of G reek Proverbs (2024)

Abstract: Proverbs carry wisdom transferred orally from generation to generation. Based on the place they were recorded, this study introduces a publicly-available and machine-actionable dataset of more than one hundred thousand Greek proverb variants. By quantifying the spatial distribution of proverbs, we show that the most widespread proverbs come from the mainland while the least widespread proverbs come primarily from the islands. By focusing on the least dispersed proverbs, we present the most frequent tokens per location and undertake a benchmark in geographical attribution, using text classification and regression (text geocoding). Our results show that this is a challenging task for which specific locations can be attributed more successfully compared to others. The potential of our resource and benchmark is showcased by two novel applications. First, we extracted terms moving the regression prediction toward the four cardinal directions. Second, we leveraged conformal prediction to attribute 3,676 unregistered proverbs with statistically rigorous predictions of locations each of these proverbs was possibly registered in.

## introduction a proverb (paroemia) is a popular saying that offers general advice or wisdom @xcite . proverbs have not only been guiding social interactions of people for thousands of years @xcite , but they continue to do so today, as is evident from proverbs such as "garbage in, garbage out" (gigo) @xcite , a popular concept in computer science. recently, computational approaches have attempted to assist paroemiography @xcite , concerned with the collection and classification of proverbs. also, paroemiology @xcite addresses questions regarding the definition, form, structure, style, content, function, meaning, and value of proverbs. ## benchmark: geographical attribution more than three thousand proverbs remain without any geographical attribution today ( §2.1). each of these proverbs may be attributed geographically by the experts, if provided with some assistance. being able to filter, for example those that are more likely to come from cyprus, we could possibly as-sist experts focusing on that location. therefore, we surmised that, to some extent, each location has its own linguistic identity, which can be modelled. to investigate this hypothesis, we opted for text classification (i.e., detecting the place of origin), and text regression (i.e., estimating the geographical coordinates of the place of origin). we note that, although our hypothesis may be true, proverbs are often edited, "regularised" linguistically by those who collect and record them, foot_6 increasing the already challenging nature of our investigation. for our experimental purposes, we created a balanced corpus, using 500 proverbs per location and 11,500 overall, foot_7 splitting randomly into train (90%), dev (5%), and test (5%) subsets. foot_8 ## applications our work showed that specific locations, wherein greek proverbs have been registered, have a distinctive linguistic identity, as was evidenced by our supervised learning benchmark (tables 2, 3). such locations are cyprus and skyros, which are easier to classify, most probably due to their linguistic distinctiveness (e.g., extreme palatalization; §2.4). building beyond this observation, we discuss two applications. first, we have extracted and discuss words that move the regression outcome toward the north, south, east, or west. second, we use conformal prediction to attribute unregistered proverbs. ## conclusion this work focused on greek proverbs. we have developed and publicly release the first large-scale machine-actionable dataset of greek proverbs, quantifying their spatial distribution across different locations. we used this dataset to set a benchmark in geographical attribution, approaching the task with classification and regression. a bert model did not clearly outperform its conventional counterparts in both tasks, sharing best performance with lr (classification) and elastic net (regression). to assess the benefits and explore the potential of our benchmark, we introduced two applications. first, we used elastic net to extract terms that push the prediction towards the four cardinal directions, showing that tsitakism pushes toward the south and east. second, we leveraged conformal prediction, narrowing the possible locations for 3,676 unregistered proverbs, which we release publicly along with our data and code. directions for future work comprise a thorough investigation of more distinctive local terms; the study of the propagation of more proverbs; and mining linguistic paths in more possible ways. popular legends took place, and a digital repository was created.