基于复杂网络文本表征的文学运动识别
Identification of Literary Movements Using Complex Networks to Represent Texts
摘要：
使用统计方法分析大型文本数据库有助于揭示人类行为模式，并建立文化和语言之间的历史联系。在这项研究中，我们通过将1590年至1922年出版的书籍视为复杂网络来识别文学运动，并使用多变量技术分析其指标，以生成六个书籍集群。后者对应的时间段与过去5个世纪的相关文学运动相吻合。区分不同文学风格的最重要因素是{平均最短路径长度（特别是分布的不对称性）}。此外，随着时间的推移，有一种趋势，朝着更大的平均最短路径长度，这是相关的增加句法复杂性，和一个更统一的使用的话反映在一个较小的幂律系数的词频分布。文学风格的变化也被发现是由对早期写作风格的反对所驱动的，正如用几何概念进行的分析所揭示的那样。这里采用的方法是通用的，可以扩展到分析语言和文化的一些特征。
英文摘要：
The use of statistical methods to analyze large databases of text has been useful to unveil patterns of human behavior and establish historical links between cultures and languages. In this study, we identify literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last 5 centuries. The most important factor contributing to the distinction between different literary styles was {the average shortest path length (particularly, the asymmetry of the distribution)}. Furthermore, over time there has been a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.
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。
