Building Foundations for Natural Language Processing of Historical
  Turkish: Resources and Models
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.
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Esma F. Bilgin Taşdemir
Building Foundations for Natural Language Processing of Historical
  Turkish: Resources and Models
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.
Transformer-based models for NER, dependency parsing, and POS tagging tasks using the first named entity recognition dataset and Universal Dependencies treebank for historical Turkish offer significant improvements, while also highlighting challenges like domain adaptation.
named entity recognition NER Universal Dependencies transformer-based models dependency parsing part-of-speech tagging Ottoman Text Corpus OTC