Classification: PHYSICAL SCIENCES, Applied Mathematical Sciences The optimality of syntactic dependency distances
Table 3: Summary of the statistical properties of ⟨Ω⟩ by dataset and language family. All indicates all the languages of the dataset, regardless of their family.
['Dataset', 'Family', 'Langs.', 'Min', 'Mean', 'Median', 'Max']
We define the percentage of optimization as 100⋅Ω. Notice that such percentage can be negative. In addition, the median indicates that half of languages are optimized to a 71% at least. High values of ⟨Ω⟩ are found in other families ( the average ⟨Ω⟩ is greater than 50% in the majority of families). All the values of ⟨Ω⟩ are positive except in a few cases: a Pama-Nyungan language (Warlpiri) for UD (but not SUD) and a Dravidian language (Telugu) for Stanford and Prague. However, we have found that the majority of languages are highly optimized. When evaluating the degree of optimization of languages, there is a trade-off between the breadth of the analysis and comparability. One of the broadest datasets, i.e. UD, allows one to conclude that half of languages are optimized to at least 70% but ⟨Ω⟩ ranges between −0.22 and 0.9 When controlling for annotation style using SUD, one finds that half of languages are optimized to at least 74% and languages with negative ⟨Ω⟩ have disappeared. Although annotation style within a dataset is homogeneous a priori, the source texts are not. When reducing the comparability problem using PUD, one finds that the degree of optimality ranges between 64% and 88% (Fig. are Indo-European (only Arabic, Thai, Indonesian, Japanese, Finnish, Chinese, Korean and Turkish are not). We hope that our research stimulates the development of large parallel corpora where linguistic families are more balanced. Therefore, whether DDm is a universal (exceptionless) phenomenon on a global scale or the scope of anti-DDm effects may depend on the annotation criteria.