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Weak labeling is a popular weak supervision strategy for Named EntityRecognition (NER) tasks, with the goal of reducing the necessity forhand-crafted annotations. Although there are numerous remarkable annotationtools for NER labeling, the subject of integrating weak labeling sources isstill unexplored. We introduce a web-based tool for text annotation calledSciAnnotate, which stands for scientific annotation tool. Compared tofrequently used text annotation tools, our annotation tool allows for thedevelopment of weak labels in addition to providing a manual annotationexperience. Our tool provides users with multiple user-friendly interfaces forcreating weak labels. SciAnnotate additionally allows users to incorporatetheir own language models and visualize the output of their model forevaluation. In this study, we take multi-source weak label denoising as anexample, we utilized a Bertifying Conditional Hidden Markov Model to denoisethe weak label generated by our tool. We also evaluate our annotation toolagainst the dataset provided by Mysore which contains 230 annotated materialssynthesis procedures. The results shows that a 53.7% reduction in annotationtime obtained AND a 1.6\% increase in recall using weak label denoising. Onlinedemo is available at https://sciannotate.azurewebsites.net/(demo account can befound in README), but we don't host a model server with it, please check theREADME in supplementary material for model server usage.

SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling