mlsanityData to 7 Oct 2026

Quality and Efficiency of Manual Annotation: Pre-annotation Bias

arXiv 2306.09307 ↗First posted 15 Jun 2023cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

5 of 30 references.

References · 5

  1. 2021RobeCzech: Czech RoBERTa, a monolingual contextualized language representation modelcited by 33
  2. 2018SciDTB: Discourse Dependency TreeBank for Scientific Abstractscited by 24
  3. 2020Prague Dependency Treebank -- Consolidated 1.0cited by 11
  4. 2017BKTreebank: Building a Vietnamese Dependency Treebankcited by 4
  5. 2021Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.