This work presents a thorough examination and expansion of the NIF ecosystem.

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第18710号 / 文博第668号 / 新制||文||612(附属図書館) / 31661 / 京都大学大学院文学研究科文献文化学専攻 / (主査)教授 家入 葉子, 教授 佐々木 徹, 准教授 廣田 篤彦 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DGAM

The variation of the strong genitive marker of the singular noun has been treated by diverse accounts. Still there is a consensus that it is to a large extent systematic but can be approached appropriately only if many heterogeneous factors are taken into account. Over thirty variables influencing this variation have been proposed. However, it is actually unclear how effective they can be, and above all, how they interact. In this paper, the potential influencing variables are evaluated statistically in a machine learning approach and modelled in decision trees in order to predict the genitive marking variants. Working with decision trees based exclusively on statistically significant data enables us to determine what combination of factors is decisive in the choice of a marking variant of a given noun. Consequently the variation factors can be assessed with respect to their explanatory power for corpus data and put in a hierarchized order.