Quality and Efficiency of Manual Annotation: Pre-annotation Bias
Marie Mikulová, Milan Straka, Jan Štěpánek, Barbora Štěpánková, Jan Hajic
Abstract
This paper presents an analysis of annotation using an automatic pre-annotation for a mid-level annotation complexity task - dependency syntax annotation. It compares the annotation efforts made by annotators using a pre-annotated version (with a high-accuracy parser) and those made by fully manual annotation. The aim of the experiment is to judge the final annotation quality when pre-annotation is used. In addition, it evaluates the effect of automatic linguistically-based (rule-formulated) checks and another annotation on the same data available to the annotators, and their influence on annotation quality and efficiency. The experiment confirmed that the pre-annotation is an efficient tool for faster manual syntactic annotation which increases the consistency of the resulting annotation without reducing its quality.- Anthology ID:
- 2022.lrec-1.312
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2909–2918
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.312
- DOI:
- Cite (ACL):
- Marie Mikulová, Milan Straka, Jan Štěpánek, Barbora Štěpánková, and Jan Hajic. 2022. Quality and Efficiency of Manual Annotation: Pre-annotation Bias. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2909–2918, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Quality and Efficiency of Manual Annotation: Pre-annotation Bias (Mikulová et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.lrec-1.312.pdf