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probabilistic manuscript reconstruction in preliminary experiments, showing that MLMs can recover unread sections in historical documents with 77.8% top-1 accuracy when a list of candidate words is provided.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.