“古籍文本中的文字通假现象较为常见,这不仅为人理解文意造成了困难,也是古汉语信息处理面临的一项重要挑战。为了服务于通假字的人工判别和机器处理,本文构建并开源了一个多维度的通假字资源库,包括语料库、知识库和评测数据集三个子库。其中,语料库收录11000余条包含通假现象详细标注的语料;知识库以汉字为节点,通假和形声关系为边,从字音、字形、字义多个角度对通假字与正字的属性进行加工,共包含4185个字节点和8350对关联信息;评测数据集面向古汉语信息处理需求,支持通假字检测和正字识别两个子任务的评测,收录评测数据19678条。在此基础上,本文搭建了通假字自动识别的系列基线模型,并结合试验结果分析了影响通假字自动识别的因素与改进方法。进一步地,本文探讨了该资源库在古籍整理、人文研究和文言文教学中的应用。”

With the increasing popularity of learning Chinese as a second language (L2) the development of an automatic essay scoring (AES) method specially for Chinese L2 essays has become animportant task. To build a robust model that could easily adapt to prompt changes we propose 90linguistic features with consideration of both language complexity and correctness and introducethe Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714 a low RMSE of 1.516 anda considerable Pearson correlation of 0.734. With a simple linear model we further analyze the contribution of the linguistic features to score prediction revealing the model’s interpretability and its potential to give writing feedback to users. This work provides insights and establishes asolid baseline for Chinese L2 AES studies.