研究人员开发了GraphLit，一个新颖的自监督学习框架，旨在创建丰富的文学文本表示。该框架利用动态异构人物网络（DHCNs）在特定的文本语境中模拟人物互动，将小说组织成局部图。与传统的纯文本或纯图方法相比，GraphLit在12项与人物相关的任务上表现出更高的性能，尤其在需要语境理解的任务上表现出色。该研究还探讨了DHCNs和GraphLit在文学分析中的应用，研究了叙事非线性与动态社会特征之间的关系。 AI
影响 该框架通过将文本语境与人物互动网络相结合，能够实现更细致的计算文学分析。
排序理由 该集群描述了一篇详细介绍用于分析文学文本的新颖框架和方法论的研究论文。
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arXiv:2605.28643v1 Announce Type: new Abstract: Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual context in which characters interact. We introduce Dyna…
Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual context in which characters interact. We introduce Dynamic Heterogeneous Character Networks (DHCNs), wh…
Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual context in which characters interact. We introduce Dynamic Heterogeneous Character Networks (DHCNs), wh…