# HUMAN: Hierarchical Universal Modular ANnotator

arXiv: https://arxiv.org/abs/2010.01080 · HTML: https://mlsanity.com/paper/2010.01080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online](https://mlsanity.com/paper/2204.13400/) (2022)
- [Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project](https://mlsanity.com/paper/2403.15143/) (2024)

## Abstract (arXiv)

A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.