Particularly in the life sciences, where texts are full of biomedical entities whose naming often does not follow convention and the relationships between entities may differ in subtle ways ( 1–8), annotation tools need to provide support for multiple domain experts to review and annotate, for automatic annotation comparisons, as well as for the tracking of annotation consistency. Gold-standard corpora, collections of text documents semantically annotated by domain experts, are crucial for the development and training of text-mining and information-extraction algorithms. 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. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat displays figures from the full text for the annotator's convenience. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. In response, we developed TeamTat ( ), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. However, human annotation requires considerable time, effort and expertise. Manually annotated data is key to developing text-mining and information-extraction algorithms.
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