This paper introduces foundational resources and models for natural language processing (NLP) of historical Turkish, a domain that has remained underexplored in computational linguistics. We present the first named entity recognition (NER) dataset, HisTR and the first Universal Dependencies treebank, OTA-BOUN for a historical form of the Turkish language along with transformer-based models trained using these datasets for named entity recognition, dependency parsing, and part-of-speech tagging tasks. Additionally, we introduce Ottoman Text Corpus (OTC), a clean corpus of transliterated historical Turkish texts that spans a wide range of historical periods. Our experimental results show significant improvements in the computational analysis of historical Turkish, achieving promising results in tasks that require understanding of historical linguistic structures. They also highlight existing challenges, such as domain adaptation and language variations across time periods. All of the presented resources and models are made available at https://huggingface.co/bucolin to serve as a benchmark for future progress in historical Turkish NLP.

本研究针对历史土耳其语这一在计算语言学领域中尚未被充分探索的领域，提出了基础资源和模型。研究中首次推出了历史土耳其语命名实体识别数据集HisTR和首次建立的Universal Dependencies语法树库OTA-BOUN，以及利用这些数据集训练的变压器模型，显著提高了对历史土耳其语的计算分析能力，为今后的研究提供了基准和契机。

为历史土耳其语自然语言处理建立基础资源和模型