A post-pandemic world resulted in economic upheaval, particularly for the cities' communities. While significant work in NLP4PI focuses on national and international events, there is a gap in bringing such state-of-the-art methods into the community development field. In order to help with community development, we must learn about the communities we develop. To that end, we propose the task of community learning as a computational task of extracting natural language data about the community, transforming and loading it into a suitable knowledge graph structure for further downstream applications. We study two particular cases of homelessness and education in showing the visualization capabilities of a knowledge graph, and also discuss other usefulness such a model can provide.
翻译:后大片世界造成了经济动荡,特别是城市社区的经济动荡。虽然全国人口和人口调查4PI在国家和国际事件上做了大量工作,但在将这种最先进的方法纳入社区发展领域方面还存在差距。为了帮助社区发展,我们必须了解我们所发展的社区。为此,我们提议把社区学习的任务作为计算任务,即提取关于社区的自然语言数据,将其转化和装入适当的知识图表结构,供下游进一步应用。我们研究了两个无家可归和教育案例,以显示知识图的可视化能力,并讨论了这种模式可以提供的其他有用性。