@inproceedings{silva-etal-2022-qa4ie,
title = "{QA}4{IE}: A Quality Assurance Tool for Information Extraction",
author = "Silva, Rafael Jimenez and
Gedela, Kaushik and
Marr, Alex and
Desmet, Bart and
Rose, Carolyn and
Zhou, Chunxiao",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.478",
pages = "4497--4503",
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 \url{https://github.com/CC-RMD-EpiBio/QA4IE}.",
}
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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.</abstract>
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%0 Conference Proceedings
%T QA4IE: A Quality Assurance Tool for Information Extraction
%A Silva, Rafael Jimenez
%A Gedela, Kaushik
%A Marr, Alex
%A Desmet, Bart
%A Rose, Carolyn
%A Zhou, Chunxiao
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F silva-etal-2022-qa4ie
%X 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.
%U https://aclanthology.org/2022.lrec-1.478
%P 4497-4503
Markdown (Informal)
[QA4IE: A Quality Assurance Tool for Information Extraction](https://aclanthology.org/2022.lrec-1.478) (Silva et al., LREC 2022)
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.