Annotation Process

An Annotation Process is a information processing process to create annotated artifacts.

• Context:
  • It can (typically) contain Annotation Tasks.
  • It can (typically) involve Annotators (in an annotation team).
  • It can (typically) require a well-defined Annotation Scheme to provide guidelines and standards for the annotations.
  • It can (often) be represented by an Annotation Process Model.
  • It can (often) be managed by an Annotation Operations Manager.
  • It can (often) utilize Annotation Tools to streamline and enhance the annotation process.
  • It can (often) require a Quality Control Process to ensure the accuracy and consistency of the annotations.
  • It can range from being a Manual Annotation Process conducted by humans to a Semi-automated Annotation Process or Fully Annotation Process.
  • ...
• Example(s):
  • a Data Annotation Process, such as a Medical Record Annotation Process.
  • a Text Annotation Process, such as a Contracts Annotation Process.
  • an Image Annotation Process, such as an Autonomous Vehicle Training Data Annotation Process.
  • a Speech Annotation Process, such as a Voice Recognition Training Data Annotation Process.
  • ...
• Counter-Example(s):
  • A Data Entry Process, which involves entering data without necessarily adding interpretative or descriptive labels.
  • A Transcription Process, which involves converting speech or audio into text without additional annotation.
• See: Annotation Task, Annotation Process Model, Annotation Operations Manager, Annotator, Data Annotation Process, Text Annotation Process, Image Annotation Process, Speech Annotation Process, Annotation Tools, Quality Control Process, Machine Learning, Annotation Scheme.

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References

2024

• (Tan et al., 2024) ⇒ Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, and Huan Liu. (2024). "Large Language Models for Data Annotation: A Survey." arXiv preprint arXiv:2402.13446. [arXiv](https://arxiv.org/abs/2402.13446).
  • NOTES: This survey explores the use of large language models (LLMs) for data annotation, discussing various techniques like zero-shot and few-shot prompts, and highlighting the potential for LLMs to generate high-quality, context-sensitive annotations.
  • QUOTE: “Data annotation generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field.”

2018

• (Gururangan et al., 2018) ⇒ Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. (2018). "Annotation Artifacts in Natural Language Inference Data." In: *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)*. Association for Computational Linguistics, New Orleans, Louisiana. [ACL Anthology](https://aclanthology.org/N18-2017).
  • NOTES: This study highlights the presence of annotation artifacts in natural language inference datasets, which can sometimes make it possible to predict labels by looking only at the hypothesis. This suggests the need for improved annotation protocols to avoid overestimating model performance.

2016

• (Finlayson & Erjavec, 2016) ⇒ Mark A. Finlayson, and Tomaž Erjavec. (2016). "Overview of Annotation Creation: Processes & Tools." In: James Pustejovsky and Nancy Ide (eds.) *Handbook of Linguistic Annotation*. New York: Springer. [arXiv:1602.05753](https://doi.org/10.48550/arXiv.1602.05753).
  • NOTES: This chapter outlines the complex endeavor of creating linguistic annotations, detailing necessary capabilities and common problems with annotation tools. It emphasizes the importance of tool support for high-quality, reusable annotations.

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