While the linguistic shifts between Ottoman and modern Turkish are well-documented qualitatively, quantitative analyses remain scarce. This study addresses this by conducting a comparative computational analysis using two Universal Dependencies treebanks: OTA-DUDU for Ottoman Turkish and TR-BOUN for modern Turkish. By employing descriptive statistics and a log-likelihood ratio test, we demonstrate the change and quantify the magnitude of diachronic variation. The analysis yields three primary statistical findings. First, our data reveals a 77% compliance rate with labial vowel harmony for suffixes, while this value is 98% in modern Turkish. This discrepancy can be explained by the presence of rounding in Ottoman Turkish, which disappears in modern Turkish. On the other hand, the compliance rate of palatal vowel harmony is quite high for both languages, 96% for Ottoman Turkish and 99% for modern Turkish. Second, some suffixes, such as the converb -(y)Ip and the dative infinitive -mAyA, changed by reducing their allomorphs in modern Turkish. Third, we demonstrate that Arabic and Persian pluralization rules, which constituted 28% of plural nouns in Ottoman Turkish, lost their pluralizing function in modern Turkish, although the words remain with singular meaning.

This paper introduces three foundational contributions to Digital Ottoman Turkish Studies. It presents: (1) three masked language models (MLMs) trained on over 11 million words from 144 works spanning from the 15th to 20th century, (2) a state-of-the-art Named Entity Recognition (NER) model (F1 = 89.94%) trained on 9,960 manually annotated entities, and (3) a state-of-the-art Universal Dependency (UD) parsing model for Ottoman Turkish. This work differs from others by deploying IJMES-transliterated documents for training and evaluation in order to prevent loss of information due to the change of the script from Perso-Arabic to Latin. The paper further explores probabilistic manuscript reconstruction in preliminary experiments, showing that MLMs can recover unread sections in historical documents with 77.8% top-1 accuracy when a list of candidate words is provided. Followed by a discussion, the paper outlines the future directions as building century-aware MLMs and expanding the training data across genres to enhance model generalization.

This paper introduces a recently released Ottoman Turkish (ota) treebank in Universal Dependencies (UD) style, DUDU. The DUDU Treebank consists of 1,064 automatically annotated and manually corrected sentences. The texts were manually collected from various academic or literary sources available on the Internet. Following preprocessing, the sentences were annotated using a MaCHAMP-based neural network model utilizing the large language model (LLM) architecture and manually corrected. The treebank became publicly available with the 2.14 release, and future steps involve expanding the treebank with more data and refining the annotation scheme. The treebank is the first and only treebank that utilizes the IJMES transliteration alphabet. The treebank not only gives insight on Ottoman Turkish lexically, morphologically, and syntactically, but also provides a small but robust test set for future computational models for Ottoman Turkish.