Quality and Efficiency of Manual Annotation: Pre-annotation Bias
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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
- 2021RobeCzech: Czech RoBERTa, a monolingual contextualized language representation modelcited by 33
- 2018SciDTB: Discourse Dependency TreeBank for Scientific Abstractscited by 24
- 2020Prague Dependency Treebank -- Consolidated 1.0cited by 11
- 2017BKTreebank: Building a Vietnamese Dependency Treebankcited by 4
- 2021Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Datacited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 4.
8 papers.
- 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Modelscited by 291
- 2024eRST: A Signaled Graph Theory of Discourse Relations and Organizationcited by 13
- 2025Bias in the Loop: How Humans Evaluate AI-Generated Suggestionscited by 4
- 2026A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extractioncited by 3
- 2025From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learningcited by 1
- 2026CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detectioncited by 0
- 2026TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speechcited by 0
- 2026Prague Dependency Treebank -- Consolidated 2.0: Enriching a Complex Annotation Schemecited by 0
- 2026Prague Dependency Treebank -- Consolidated 2.0: Enriching a Complex Annotation Scheme23 Jun 2026
- 2026TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech1 Jun 2026
- 2026CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection5 Apr 2026
- 2026A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction4 Feb 2026
- 2025From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning28 Oct 2025
- 2025Bias in the Loop: How Humans Evaluate AI-Generated Suggestions10 Sep 2025
- 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models16 Jan 2025
- 2024eRST: A Signaled Graph Theory of Discourse Relations and Organization20 Mar 2024