QA4IE: A Quality Assurance Tool for Information Extraction
Rafael Jimenez Silva, Kaushik Gedela, Alex Marr, Bart Desmet, Carolyn Rose, Chunxiao Zhou
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Abstract
Quality assurance (QA) is an essential though underdeveloped part of the data annotation process. Although QA is supported to some extent in existing annotation tools, comprehensive support for QA is not standardly provided. In this paper we contribute QA4IE, a comprehensive QA tool for information extraction, which can (1) detect potential problems in text annotations in a timely manner, (2) accurately assess the quality of annotations, (3) visually display and summarize annotation discrepancies among annotation team members, (4) provide a comprehensive statistics report, and (5) support viewing of annotated documents interactively. This paper offers a competitive analysis comparing QA4IE and other popular annotation tools and demonstrates its features, usage, and effectiveness through a case study. The Python code, documentation, and demonstration video are available publicly at https://github.com/CC-RMD-EpiBio/QA4IE.