Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages
Table 2: MRR results for step 4. See text for details.
['language', 'past', 'present', 'future', 'all']
We make three contributions. (i) Our basic hypotheses are H1 and H2. (H1) For an important linguistic feature, there exist a few languages that mark it overtly and easily recognizably. (H2) It is possible to project overt markers to overt and non-overt markers in other languages. Based on these two hypotheses we design SuperPivot, a new method for analyzing highly parallel corpora, and show that it performs well for the crosslingual analysis of the linguistic phenomenon of tense. (ii) Given a superparallel corpus, SuperPivot can be used for the analysis of any low-resource language represented in that corpus. (iii) We extend Michael Cysouw’s pioneering work on typological analysis using parallel corpora by overcoming several limiting factors. The most important is that Cysouw’s method is only applicable if markers of the relevant linguistic feature are recognizable on the surface in all languages. In contrast, we only assume that markers of the relevant linguistic feature are recognizable on the surface in a small number of languages. The rank for a particular ranking of n-grams is the first n-gram that is highly correlated with the relevant tense; e.g., character subsequences of the name “Paulus” are evaluated as incorrect, the subsequence “-ed” in English as correct for past. MRR is averaged over all n-gram sizes, 2≤n≤6. Chinese has consistent tense marking only for future, so results are poor. Russian and Polish perform poorly because their central grammatical category is aspect, not tense. The poor performance on Arabic is due to the limits of character n-gram features for a “templatic” language.