标记网络子图揭示了书面文本中的文体微妙之处
Labelled network subgraphs reveal stylistic subtleties in written texts
摘要：
大量的数据和计算能力的增加允许从多个角度分析文本，包括将文本表示为复杂网络。网络的节点代表单词，而边代表某种关系，通常是单词共现。即使网络表示已被应用于研究一些任务，这种方法通常不与传统的模型依赖于统计范式相结合。由于网络模型能够掌握文本模式，我们设计了一种混合分类器，称为标记子图，它将常见单词的频率与网络拓扑中的小结构（称为基序）相结合。我们的方法是说明在两个方面，作者归属和识别。在前者中，分析了一组由不同作者撰写的小说。为了识别加拿大语，加拿大议会和欧洲议会的文本被分类为原始和翻译实例。我们的研究结果表明，标记子图能够表示文本，它应该进一步探索在其他任务，如文本复杂性分析，语言熟练度，和机器翻译。
英文摘要：
The vast amount of data and increase of computational capacity have allowed the analysis of texts from several perspectives, including the representation of texts as complex networks. Nodes of the network represent the words, and edges represent some relationship, usually word co-occurrence. Even though networked representations have been applied to study some tasks, such approaches are not usually combined with traditional models relying upon statistical paradigms. Because networked models are able to grasp textual patterns, we devised a hybrid classifier, called labelled subgraphs, that combines the frequency of common words with small structures found in the topology of the network, known as motifs. Our approach is illustrated in two contexts, authorship attribution and translationese identification. In the former, a set of novels written by different authors is analyzed. To identify translationese, texts from the Canadian Hansard and the European parliament were classified as to original and translated instances. Our results suggest that labelled subgraphs are able to represent texts and it should be further explored in other tasks, such as the analysis of text complexity, language proficiency, and machine translation.
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。
