跨学科博士论文的动态词汇特征:一种文本挖掘方法

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS
Wei Xiao, S. Sun
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引用次数: 14

摘要

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领域博士论文的词汇特征及其动态变化。采用TTR、h-point、R1和作者观点四个定量指标对150篇博士论文(每个学科50篇)进行了分析。尽管h点和作者的观点在不同学科之间表现出不明显的差异,但TTR和R1的结果确实揭示了人文科学和自然科学论文之间的鲜明对比。虽然人文学科论文的后半部分表现出显著更高的词汇多样性水平,TTR更高,但自然科学论文的前半部分内容词往往更丰富,R1更高。与此同时,社会科学的论文似乎更为温和,其特点处于中间位置。这项研究不仅对拓宽定量语言学方法的应用范围,而且对学术写作(尤其是博士论文写作)的教学和实践都有启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Lexical Features of PhD Theses across Disciplines: A Text Mining Approach
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.
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来源期刊
CiteScore
2.90
自引率
7.10%
发文量
7
期刊介绍: The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.
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