Abstract
The noncausal/causal alternation is the pairing of two verb forms that refer to the same core event but differ in the absence vs. presence of a causer for this event (e.g. rise vs. raise, open (intr.) vs. open (tr.), die vs. kill). Languages differ in their overall preferences among the possible strategies for coding this alternation. This study uses machine-learning methods (clustering and tree-based computational classifiers) to investigate the predictive power of the noncausal/causal alternation for the genealogical affiliation of 38 languages belonging to the Atlantic, Mande and Mel families. The languages studied here belong to different contact areas in Senegal and its surroundings. The three families are all affiliated to the Niger-Congo phylum but display quite different typological profiles. The present paper elaborates on an earlier study that used a standard list of 18 verb pairs to establish the coding strategies in these languages. Apart from highlighting which coding strategies are favored in each family, our quantitative analyses show that the family affiliation of the 38 languages can be predicted with an accuracy above the majority baseline based on the information of the noncausal/causal alternation in the 18 verb pairs, but that the predictive power of verb pairs 1‑9 is generally lower than the one of verb pairs 10‑18. Our results confirm the hypothesis that the first group of verb pairs shows universal rather than lineage-specific tendencies concerning the noncausal/causal alternation. Furthermore, our analyses identify which of the 18 verb pairs (and their correlated coding strategies) have the highest predictive power. This study opens new avenues for identifying the relevant synchronic data for genealogical classification in historical linguistics. Future studies could replicate the same analysis in different language families to assess if our results are universal or specific to some language families.
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