Burrows' Delta在中文诗歌文本中的效果如何？
How does Burrows' Delta work on medieval Chinese poetic texts?
AI总结：
本研究探讨Burrows' Delta作者归属方法在语法与文字体系不同的中古汉语诗歌文本中的有效性，实验证实该方法可准确识别唐代诗人，验证了其跨语言适用性。
摘要：
Burrows' Delta于2002年提出，已被证实是作者归属研究的有效工具。尽管现有研究涉及的语言各不相同，但它们大多属于同一语法类型，采用相同的文字表音原则：即音位字母系统，通过空格实现词间分隔。本文旨在探讨该归属方法应用于语法结构不同、文字体系基于完全不同原则的语言文本时的效果。目前针对Delta方法在中文文本上有效性的研究，远少于针对欧洲语言文本的相关研究。笔者认为，汉学界对Delta方法关注度较低，与中古汉语诗歌研究领域的学科结构有关。基于文本间距离的聚类效果十分理想。Delta方法的实验结果显示，聚类中同一作者的样本相似度最高，且Delta从未混淆不同诗人的作品。尽管笔者采用了非传统路径，将Delta方法应用于适配性较低的语言，该方法仍展现出了有效性。利用Delta可准确识别唐代诗人，且针对欧洲标准语言作者观察到的经验模式再次得到验证。
英文摘要：
Burrows' Delta was introduced in 2002 and has proven to be an effective tool for author attribution. Despite the fact that these are different languages, they mostly belong to the same grammatical type and use the same graphic principle to convey speech in writing: a phonemic alphabet with word separation using spaces. The question I want to address in this article is how well this attribution method works with texts in a language with a different grammatical structure and a script based on different principles. There are fewer studies analyzing the effectiveness of the Delta method on Chinese texts than on texts in European languages. I believe that such a low level of attention to Delta from sinologists is due to the structure of the scientific field dedicated to medieval Chinese poetry. Clustering based on intertextual distances worked flawlessly. Delta produced results where clustering showed that the samples of one author were most similar to each other, and Delta never confused different poets. Despite the fact that I used an unconventional approach and applied the Delta method to a language poorly suited for it, the method demonstrated its effectiveness. Tang dynasty poets are correctly identified using Delta, and the empirical pattern observed for authors writing in European standard languages has been confirmed once again.
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。
