Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models
It addresses the underexplored domain of historical Turkish NLP, providing foundational resources and models for researchers and linguists, though it is incremental as it applies existing methods to new data.
This paper tackles the lack of computational resources for historical Turkish by introducing the first named entity recognition dataset, Universal Dependencies treebank, and a clean text corpus, along with transformer-based models for NLP tasks, achieving promising results in analyzing historical linguistic structures.
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.