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

BERT with Character - 知识图谱注入神经语言模型来分析文学人物的描述 (LitBERT)

• 批准号: 529659926
• 负责人: Professor Dr. Andreas Hotho
• 金额: --
• 依托单位: Institut für Deutsche Philologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/529659926?language=en
• 关键词: BERT; Character; Knowledge; Graph; infused

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).

该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，它使用计算语言学，计算机科学及其自身传统的广泛工具来分析大量的文学文本。在我们的项目中，我们专注于计算文学分析的字符作为一个最重要的描述符的叙事和戏剧文本。我们的项目将探讨文字描述的字符的内部和外部的特点，行动和进一步字符的具体信息，使用知识诱导的语言模型。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字符关注的“文学”语言模型（“LitBERT”），用于自动文学分析。该项目将显著推进语言模型和知识图相结合的最新技术水平，展示如何通过（a）整合知识图和（B）基于使用语言模型的文本分析丰富特定领域的知识图，来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来改进对长篇文本（如小说）的处理，例如文本世界中字符的表示和类型（即，《圣经》中描述的世界）。