Quality and Efficiency of Manual Annotation: Pre-annotation Bias
This addresses annotation efficiency and quality for NLP researchers, but is incremental as it confirms existing expectations about pre-annotation.
This paper analyzed how automatic pre-annotation affects manual dependency syntax annotation, finding that it speeds up annotation and increases consistency without reducing quality.
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.