Classification: PHYSICAL SCIENCES, Applied Mathematical Sciences The optimality of syntactic dependency distances
Table 4: The significance of ⟨Ω⟩. l0 is the number of languages available from the dataset, fH, is the number of languages where ⟨Ω⟩ is significantly large after applying Holm’s correction to the languages counted in l0, and Exceptions is the number of the languages counted in l0 where ⟨Ω⟩ is not significantly large. The number attached to the language name indicates the corrected p-value.
['Dataset', '[ITALIC] l0', '[ITALIC] fH', 'Exceptions', 'Family', 'Languages']
Interestingly, we did not find any language with a significantly small ⟨Ω⟩ although ⟨Ω⟩<0 in a few languages as reported above. In contrast, previously existing optimality scores only indicate that dependency distances are not significantly small in Warlpiri (SI Appendix), suggesting that Ω has a slightly greater capacity to detect exceptions than other scores. However, all these issues should be the subject of future research. However, such interpretation should be examined in light of two observations. This issue is developed further below. These considerations notwithstanding, the true number of exceptions could be larger but hidden by biases in the families in our dataset. Second, we cannot exclude the possibility that the cases where DDm does not surface are not due to genuine properties of the languages but rather a case of the comparability or homogeneity problem: e.g. a consequence of the annotation criteria beyond those specified in the dataset or biases in the sample of sentences forming the collection (for instance, the author or the genre), that hide the manifestation of DDm (this is issue is also developed below). 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.