论文标题
叙事知识图中的关系聚类
Relation Clustering in Narrative Knowledge Graphs
论文作者
论文摘要
当应对小说或短篇小说等文学文本时，以知识图的形式提取结构化信息可能会受到与小说中角色相对应的实体之间的大量可能关系以及随之而来的障碍，从而收集有关它们的监督信息。此处是作为一个无监督的任务来解决的，这些任务是由变形金刚赋予的：原始文本中的关系句子被嵌入（带有Sbert）并聚集在一起，以便将语义上相似的关系合并在一起。最终（带有BART）和从摘要中提取的描述性标签最终总结了同一集群中的所有句子。初步测试表明，这种聚类可能会成功地检测到相似的关系，并为半监督方法提供了有价值的预处理。
When coping with literary texts such as novels or short stories, the extraction of structured information in the form of a knowledge graph might be hindered by the huge number of possible relations between the entities corresponding to the characters in the novel and the consequent hurdles in gathering supervised information about them. Such issue is addressed here as an unsupervised task empowered by transformers: relational sentences in the original text are embedded (with SBERT) and clustered in order to merge together semantically similar relations. All the sentences in the same cluster are finally summarized (with BART) and a descriptive label extracted from the summary. Preliminary tests show that such clustering might successfully detect similar relations, and provide a valuable preprocessing for semi-supervised approaches.
