从读者的文学书评中提取人物和关系的自动管道Goodreads.com
An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com
摘要：
读者在社交媒体上对文学小说的评论，特别是那些持续的、专门的论坛上的评论，创造了底层的叙事框架，反过来又受到底层叙事框架的驱动。在他们对一部小说的评论中，读者通常只包括一部分人物和他们的关系，从而提供了一个有限的视角。然而，总的来说，这些评论捕捉到了一个潜在的叙事框架，包括不同的行动者（人，地点，事物），他们的角色和互动，我们称之为“共识叙事框架”。我们以演员关系故事图的形式表示这个框架。提取这个图是一个具有挑战性的计算问题，我们把它作为一个潜在的图形模型估计问题。帖子和评论被视为隐藏叙事框架的子图/网络的样本。受Greimas的定性叙事理论的启发，我们制定了一个图形生成机器学习（ML）模型，其中节点代表行动者，节点之间的多边和自循环捕获特定于上下文的关系。我们开发了一个连锁自动化方法的管道，以提取关键行为体及其关系，并将其应用于Goodreads.com上发布的数千条评论和评论。我们从SparkNotes手动获得地面实况叙事框架，然后使用词嵌入工具将地面实况网络中的关系与我们提取的网络进行比较。我们发现，我们的自动化方法生成了高度准确的共识叙事框架：对于我们的四部目标小说，每部小说大约有2900条评论，我们报告的重要关系的平均覆盖率/召回率> 80%，平均边缘检测率> 89%。这些提取的叙事框架可以深入了解人们（或一类人）如何阅读，以及他们如何向他人讲述他们所读的内容。
英文摘要：
Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of > 80% and an average edge detection rate of >89\%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others.
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。
