语言复杂性的复杂网络分析
Complex networks analysis of language complexity
摘要：
统计物理学的方法，如涉及复杂网络的方法，越来越多地用于语言现象的定量分析。在本文中，我们表示的文本与同现网络的不同程度的简化，发现拓扑规则性与文本的复杂性呈负相关。此外，在不太复杂的文本中，概念之间的距离，表示为节点，往往会减少。复杂的网络度量与多元模式识别技术，这使我们能够区分原始文本和简化版本。对于每一个原始文本，随着简化操作的增加，手工生成两个简化版本。正如预期的那样，对于高度简化的版本，区分更容易，其中最相关的指标是节点强度，最短路径和多样性。此外，更高层次的网络指标，提高了复杂文本的歧视，从而指出考虑更广泛的背景下的概念的有用性。尽管这种区分的准确率不如使用深层语言知识的方法那么高，但当评估复杂性对于保证阅读能力有限的读者的可及性至关重要时，复杂网络方法仍然可以用于快速筛选文本
英文摘要：
Methods from statistical physics, such as those involving complex networks, have been increasingly used in quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability
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。
