eFontes：中世纪拉丁文本的词性标注与词形还原——一项跨体裁调查
eFontes. Part of Speech Tagging and Lemmatization of Medieval Latin Texts.A Cross-Genre Survey
AI总结：
本文提出基于Transformers的eFontes模型，用于中世纪拉丁文本的词形还原、词性标注和形态特征确定，在UD及自建波兰中世纪拉丁语料库上训练，取得较高准确率，并展望扩展至命名实体识别。
摘要：
本研究介绍了用于中世纪拉丁文本自动语言标注的eFontes模型，重点关注词形还原、词性标注和形态特征确定。利用Transformers库，这些模型在Universal Dependencies (UD)语料库和新开发的波兰中世纪拉丁语eFontes语料库上进行了训练。该研究评估了模型性能，应对了诸如正字法变异和拉丁化本土术语整合等挑战。模型取得了较高的准确率：词形还原为92.60%，词性标注为83.29%，形态特征确定为88.57%。研究结果强调了高质量标注语料库的重要性，并提出了未来的改进方向，包括将模型扩展到命名实体识别。
英文摘要：
This study introduces the eFontes models for automatic linguistic annotation of Medieval Latin texts, focusing on lemmatization, part-of-speech tagging, and morphological feature determination. Using the Transformers library, these models were trained on Universal Dependencies (UD) corpora and the newly developed eFontes corpus of Polish Medieval Latin. The research evaluates the models' performance, addressing challenges such as orthographic variations and the integration of Latinized vernacular terms. The models achieved high accuracy rates: lemmatization at 92.60%, part-of-speech tagging at 83.29%, and morphological feature determination at 88.57%. The findings underscore the importance of high-quality annotated corpora and propose future enhancements, including extending the models to Named Entity Recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
