QA4IE: A Quality Assurance Tool for Information Extraction

Rafael Jimenez Silva, Kaushik Gedela, Alex Marr, Bart Desmet, Carolyn Rose, Chunxiao Zhou


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.
Anthology ID:
2022.lrec-1.478
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4497–4503
Language:
URL:
https://aclanthology.org/2022.lrec-1.478
DOI:
Bibkey:
Cite (ACL):
Rafael Jimenez Silva, Kaushik Gedela, Alex Marr, Bart Desmet, Carolyn Rose, and Chunxiao Zhou. 2022. QA4IE: A Quality Assurance Tool for Information Extraction. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4497–4503, Marseille, France. European Language Resources Association.
Cite (Informal):
QA4IE: A Quality Assurance Tool for Information Extraction (Silva et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.478.pdf
Code
 cc-rmd-epibio/qa4ie