论文标题
使用多层网络对科学学术文本的定量话语凝聚力分析
Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks
论文作者
论文摘要
话语凝聚力有助于文本理解，并帮助读者形成连贯的叙述。在这项研究中，我们旨在使用多层网络表示并量化文档的写作质量来分析科学学术文本中的话语内聚力。利用科学学术文本的层次结构，我们设计了部分级别和文档级指标，以评估文本中词汇内聚力的程度。我们使用公开可用的数据集以及一组策划的对比示例来验证所提出的指标，通过将它们与使用现有内聚力分析工具计算的精选索引进行比较。我们观察到，所提出的指标与现有内聚力指数相关。 我们还提出了一个分析框架CHIAA（作者再次检查），以在截面级别和文档级指标的帮助下向作者提供指示以进行手稿的潜在改进。拟议的CHIAA框架为作者提供了清晰而精确的处方，用于通过具有凝聚力差距的文本定位区域来改善写作。我们使用实验数据集中的凝聚力缺陷文本摘录中的简洁示例来证明CHIAA框架的功效。
Discourse cohesion facilitates text comprehension and helps the reader form a coherent narrative. In this study, we aim to computationally analyze the discourse cohesion in scientific scholarly texts using multilayer network representation and quantify the writing quality of the document. Exploiting the hierarchical structure of scientific scholarly texts, we design section-level and document-level metrics to assess the extent of lexical cohesion in text. We use a publicly available dataset along with a curated set of contrasting examples to validate the proposed metrics by comparing them against select indices computed using existing cohesion analysis tools. We observe that the proposed metrics correlate as expected with the existing cohesion indices. We also present an analytical framework, CHIAA (CHeck It Again, Author), to provide pointers to the author for potential improvements in the manuscript with the help of the section-level and document-level metrics. The proposed CHIAA framework furnishes a clear and precise prescription to the author for improving writing by localizing regions in text with cohesion gaps. We demonstrate the efficacy of CHIAA framework using succinct examples from cohesion-deficient text excerpts in the experimental dataset.
