首个历史土耳其语NLP资源与模型，助力古籍文本分析
Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models
- 构建首个历史土耳其语命名实体数据集与句法树库
- 在命名实体识别等任务上达90.29% F1，表现优异
- 适合历史语言学、数字人文研究者使用
本文为历史土耳其语自然语言处理建立了基础资源与模型，该领域在计算语言学中长期被忽视。我们首次推出命名实体识别数据集HisTR和通用依存树库OTA-BOUN，以及基于这些数据训练的Transformer模型，涵盖命名实体识别、依存句法分析和词性标注任务。同时发布奥斯曼文转写语料库OTC，覆盖广泛历史时期。实验结果表明，模型在理解历史语言结构的任务中表现突出：命名实体识别达90.29% F1，依存句法分析LAS为73.79%，词性标注达94.98% F1。研究也揭示了领域适应与不同时期语言差异等挑战。所有资源与模型均已公开于https://hf.co/bucolin，可作为未来研究基准。
原文摘要 · Abstract (English)
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. Furthermore, we introduce the Ottoman Text Corpus (OTC), a clean corpus of transliterated historical Turkish texts that spans a wide range of historical periods. Our experimental results demonstrate prominent improvements in the computational analysis of historical Turkish, achieving strong performance on tasks that require understanding of historical linguistic structures -- specifically, 90.29% F1 in named entity recognition, 73.79% LAS for dependency parsing, and 94.98% F1 for part-of-speech tagging. They also highlight existing challenges, such as domain adaptation and language variations between time periods. All the resources and models presented are available at https://hf.co/bucolin to serve as a benchmark for future progress in historical Turkish NLP.
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