# TeamTat: a collaborative text annotation tool

arXiv: https://arxiv.org/abs/2004.11894 · HTML: https://mlsanity.com/paper/2004.11894/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [CARE: Extracting Experimental Findings From Clinical Literature](https://mlsanity.com/paper/2311.09736/) (2023)
- [Traceable LLM-based validation of statements in knowledge graphs](https://mlsanity.com/paper/2409.07507/) (2024)
- [CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction](https://mlsanity.com/paper/2307.00769/) (2023)
- [Repurposing Annotation Guidelines to Instruct LLM Annotators: A Case Study](https://mlsanity.com/paper/2510.12835/) (2025)
- [Enhancing Biomedical Relation Extraction with Directionality](https://mlsanity.com/paper/2501.14079/) (2025)
- [Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature](https://mlsanity.com/paper/2305.17353/) (2023)
- [EnzChemRED, a rich enzyme chemistry relation extraction dataset](https://mlsanity.com/paper/2404.14209/) (2024)
- [Refining and Reusing Annotation Guidelines for LLM Annotation](https://mlsanity.com/paper/2605.20809/) (2026)
- [MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales](https://mlsanity.com/paper/2608.10974/) (2026)

## Abstract (arXiv)

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for image display, project management, and multi-user team annotation. In response, we developed TeamTat (teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC, (uploaded locally or automatically retrieved from PubMed or PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotators convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus-quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.