GraphLit：面向文学研究的文本增强动态人物网络表示学习
GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
AI总结：
提出动态异质人物网络（DHCN）和自监督框架GraphLit，通过掩码图自编码器学习融合文本上下文的文学表示，在12个角色相关任务上优于纯文本或纯图基线。
摘要：
将文学文本表示为图或图序列的方法主要侧重于表示角色互动，而往往忽略了另一个关键方面：角色互动的文本上下文。我们引入了动态异质人物网络（DHCN），将长篇小说组织成时间局部化的异构图，将角色与其文本上下文对齐。我们从Project Gutenberg中提取了约20,000个DHCN，并提出了GraphLit，这是一个自监督学习框架，通过掩码图自编码器目标学习丰富的文学表示。在广泛的12个角色相关任务中，GraphLit优于纯文本和纯图基线，特别是在需要上下文理解的任务上。最后，我们通过研究叙事非线性和动态社会特征之间的联系，展示了DHCN和GraphLit在文学分析中的适用性。
英文摘要：
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), which organize long novels into temporally localized heterogeneous graphs that align characters with their textual contexts. We extract around 20,000 DHCNs from Project Gutenberg, and propose GraphLit, a self-supervised learning framework that learns rich literary representations through a masked graph autoencoder objective. Across a wide range of 12 character-related tasks, GraphLit improves over text-only, graph-only and prior hybrid baselines. Ablations over different kinds of dynamic graph structures and architectural elements show that grounding characters in their context is the main performance driver, while explicitly encoding narrative order and character relationships provide task-dependent improvements. Finally, we demonstrate the applicability of DHCNs and GraphLit for literary analysis by studying the link between narrative non-linearity and dynamic social features.
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。
