ABSTRACT This study employed a text mining method to investigate the lexical features and their dynamic changes of PhD theses across the natural sciences, social sciences and humanities. Four quantitative indices, i.e. TTR, h-point, R1 and writer’s view, were employed to analyze 150 PhD theses (50 theses from each discipline). Although h-point and writer’s view were found counter-intuitively to show insignificant variation across disciplines, the results of TTR and R1 did reveal sharp contrasts between theses in humanities and natural sciences. While the second half of humanities theses showed a significantly higher level of lexical diversity, indicated by higher TTR, theses in natural sciences tended to be richer in content words in the first half, indicated by a higher R1. Meanwhile, theses in social sciences seemed to be more moderate, with features lying in the middle position. This study has implications not only for the widening of applications of quantitative linguistic methods but also for academic writing (especially PhD thesis writing) instruction and practice.

中文翻译：

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跨学科博士学位论文的动态词汇特征：一种文本挖掘方法

摘要本研究采用文本挖掘方法研究了自然科学，社会科学和人文科学博士学位论文的词汇特征及其动态变化。TTR，h-point，R1和作者的观点这四个定量指标用于分析150篇博士论文（每门学科有50篇论文）。尽管h-point和作者的观点被发现是违反直觉的，显示出各个学科之间的微不足道的变化，但是TTR和R1的结果确实揭示了人文学科与自然科学之间的鲜明对比。尽管人文科学的后半部分显示出较高的词汇多样性水平（以较高的TTR表示），但自然科学中的这些主题在上半部分倾向于以更丰富的单词表示（R1较高）。同时，社会科学领域的论文似乎比较温和，特征位于中间位置。这项研究不仅对定量语言方法的广泛应用具有启示意义，而且对学术写作（特别是博士学位论文写作）的指导和实践也具有重要意义。