同心网络对称性在词邻接网络中把握作者的风格
Concentric network symmetry grasps authors' styles in word adjacency networks
摘要：
书面文本的几个特征已经从网络模型的统计分析中推断出来。尽管许多网络测量已经被用来研究不同复杂程度的文本属性，但一些文本方面却被忽视了。在本文中，我们研究了词邻接网络的对称性，这是一种众所周知的文本图形表示方法。对几部小说中对称分布的统计分析表明，大多数单词都没有显示出对称的连接模式。更具体地说，合并的对称性显示出类似于普遍存在的幂律分布的分布。我们的实验还表明，所研究的指标与其他传统的网络度量（如度或介数中心性）不相关。对称性测量的有效性在作者归因任务中得到了验证。有趣的是，我们发现特定的作者更喜欢特定类型的对称图案。因此，在由8位作者撰写的书籍组成的数据集中，82.5%的情况下可以准确识别书籍的作者身份。由于所提出的文本分析方法是对传统方法的补充，因此它们可以用于改进文本网络的表征，这对于相关应用可能是有用的，例如那些依赖于主题词的识别和信息检索的应用。
英文摘要：
Several characteristics of written texts have been inferred from statistical analysis derived from networked models. Even though many network measurements have been adapted to study textual properties at several levels of complexity, some textual aspects have been disregarded. In this paper, we study the symmetry of word adjacency networks, a well-known representation of text as a graph. A statistical analysis of the symmetry distribution performed in several novels showed that most of the words do not display symmetric patterns of connectivity. More specifically, the merged symmetry displayed a distribution similar to the ubiquitous power-law distribution. Our experiments also revealed that the studied metrics do not correlate with other traditional network measurements, such as the degree or betweenness centrality. The effectiveness of the symmetry measurements was verified in the authorship attribution task. Interestingly, we found that specific authors prefer particular types of symmetric motifs. As a consequence, the authorship of books could be accurately identified in 82.5% of the cases, in a dataset comprising books written by 8 authors. Because the proposed measurements for text analysis are complementary to the traditional approach, they can be used to improve the characterization of text networks, which might be useful for related applications, such as those relying on the identification of topical words and information retrieval.
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。
