Abstract
Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commonsense reasoning. Such knowledge graphs are constructed through knowledge acquisition tasks such as relation extraction and knowledge graph completion. This work seeks to utilise and build on the growing body of work that uses findings from the field of natural language processing (NLP) to extract knowledge from text and build knowledge graphs. The focus of this research project is on how we can use transformer-based approaches to extract and contextualise event information, matching it to existing ontologies, to build a comprehensive knowledge of graph-based event representations. Specifically, sub-event extraction is used as a way of creating sub-event-aware event representations. These event representations are then further enriched through fine-grained location extraction and contextualised through the alignment of historically relevant quotes.
Abstract (translated)
最近的工作利用了知识 aware 的方法来实现自然语言理解、回答问题、推荐系统和其他任务。这些方法依赖于构建良好且大规模的知识图,对于许多后续应用非常有用,并利用常识推理使知识 aware 模型具有能力。这些知识图是通过关系提取和知识图完成的知识获取任务构建的。本研究的目标是利用和建立使用自然语言处理领域发现从文本中提取知识并构建知识图的方法,以建立基于图的事件表示的全面知识。具体而言,子事件提取被用来创建子事件aware的事件表示。这些事件表示随后通过精细的位置提取和通过历史相关引用的对齐进行进一步丰富。
URL
https://arxiv.org/abs/2303.04794
