Quality and Efficiency of Manual Annotation: Pre-annotation Bias
Mikulová, Marie, Straka, Milan, Štěpánek, Jan, Štěpánková, Barbora, Hajič, Jan
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Jun-15-2023
- Country:
- Oceania > Australia
- Victoria > Melbourne (0.04)
- New South Wales > Sydney (0.04)
- North America > United States
- Maryland > Baltimore (0.04)
- Pennsylvania > Philadelphia County
- Philadelphia (0.04)
- Europe
- Czechia > Prague (0.06)
- United Kingdom > England
- Greater Manchester > Manchester (0.04)
- Sweden > Uppsala County
- Uppsala (0.04)
- Netherlands > South Holland
- Dordrecht (0.04)
- Middle East > Malta
- Port Region > Southern Harbour District > Valletta (0.04)
- France > Provence-Alpes-Côte d'Azur
- Bouches-du-Rhône > Marseille (0.04)
- Bulgaria > Sofia City Province
- Sofia (0.04)
- Asia
- Oceania > Australia
- Genre:
- Research Report
- New Finding (1.00)
- Experimental Study (0.68)
- Research Report
- Technology: