BERT with Character - Knowledge Graph infused neural language models to analyse the depiction of literary characters (LitBERT)

这个项目是计算机科学和计算文学研究(CLS)之间的合作，计算文学研究是一个新兴的领域，使用来自计算语言学、计算机科学和自身传统的一系列工具来分析更多的文学文本。在我们的项目中，我们专注于对人物的计算文学分析，作为叙事和戏剧文本最重要的描述符之一。我们的项目将使用知识诱导语言模型来研究角色的内部和外部特征、动作以及进一步的角色特定信息的文本描述。我们的目标是通过从文本中提取字符信息来创建字符知识图，通过数据驱动的聚类来发现不同的字符类型，并利用这些信息来开发一个关注字符的、用于自动文学分析的语言模型(LitBERT)。该项目将极大地提高语言模型和知识图谱相结合的水平，展示如何通过(A)整合知识图谱和(B)在使用语言模型进行文本分析的基础上丰富特定领域的知识图谱，从而提高语言模型分析实体及其属性的性能。此外，我们希望通过提高语言模型表示知识的能力，如文本世界(即文本中描述的世界)中的字符的表示和类型，来改进对小说等较长文本的处理。

This project is a collaboration between computer science and computational literary studies (CLS), an emergent field which analyses larger collections of literary texts using a wide set of tools from computational linguistics, computer science and its own tradition. In our project, we focus on the computational literary analysis of character as one of the most important descriptors of narrative and dramatic texts. Our project will investigate the textual description of character's internal and external features, actions and further character specific information using knowledge induced language models. We aim to create a character knowledge graph through extracting character information from text, find different character types through data-driven clustering, and leverage this information to develop a character-attentive, "literary" language model ("LitBERT") for automatic literary analysis. The project will significantly advance the state of the art in the combination of language models and knowledge graphs, showing how to improve the performance of language models for the analysis of entities and their attributes by (a) integrating knowledge graphs and (b) enriching domain specific knowledge graphs based on text analysis using language models. Additionally, we want to improve the handling of longer texts like novels by advancing the capabilities of language models to represent knowledge, like representation and types of characters in the text world (i.e., the world described in the text).