Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective

Knowledge Extraction (KE) which aims to extract structural information from unstructured texts often suffers from data scarcity and emerging unseen types, i.e., low-resource scenarios. Many neural approaches on low-resource KE have been widely investigated and achieved impressive performance. In this paper, we present a literature review towards KE in low-resource scenarios, and systematically categorize existing works into three paradigms: (1) exploiting higher-resource data, (2) exploiting stronger models, and (3) exploiting data and models together. In addition, we describe promising applications and outline some potential directions for future research. We hope that our survey can help both the academic and industrial community to better understand this field, inspire more ideas and boost broader applications.