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Updated: Dec 22, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

TeamTat: a collaborative text annotation tool.

Rezarta Islamaj¹, Dongseop Kwon², Sun Kim¹

¹National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Nucleic Acids Research

|May 9, 2020

Summary

This summary is machine-generated.

TeamTat is a novel web-based tool designed to streamline manual data annotation for text-mining algorithms. It efficiently manages multi-user projects, enhances collaboration, and ensures high-quality biomedical data for research.

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Area of Science:

Biomedical Informatics
Computational Biology
Natural Language Processing

Background:

Manual data annotation is crucial for developing text-mining and information-extraction algorithms in the rapidly expanding biomedical literature.
Existing annotation tools often lack features for figure display, project management, and collaborative team annotation, hindering efficiency and quality.

Purpose of the Study:

To develop and present TeamTat, a novel web-based annotation tool designed to facilitate efficient and high-quality multi-user, multi-label document annotation for biomedical research.
To address the limitations of current tools by providing comprehensive support for the entire annotation lifecycle, from project setup to quality assessment.

Main Methods:

TeamTat is a web-based platform with a local setup option, supporting multi-user, multi-label document annotation.
Features include customizable annotation schemas, anonymous document distribution, various input formats (text, PDF, BioC), and BioC output.
The tool integrates figure display, independent user workspaces, project management dashboards, and inter-annotator agreement statistics for quality control.

Main Results:

TeamTat effectively manages the entire annotation production lifecycle for teams.
It supports diverse document input and BioC output formats, displays figures, and enables anonymous distribution to prevent bias.
The platform facilitates collaboration, task tracking, and corpus quality assessment through inter-annotator agreement metrics and review interfaces.

Conclusions:

TeamTat offers a robust solution for managing complex, multi-user annotation projects in the biomedical domain.
Its features enhance efficiency, ensure expert quality, and support collaborative efforts in creating high-value annotated datasets for text-mining applications.