  Weak labeling is a popular weak supervision strategy for Named Entity
Recognition (NER) tasks, with the goal of reducing the necessity for
hand-crafted annotations. Although there are numerous remarkable annotation
tools for NER labeling, the subject of integrating weak labeling sources is
still unexplored. We introduce a web-based tool for text annotation called
SciAnnotate, which stands for scientific annotation tool. Compared to
frequently used text annotation tools, our annotation tool allows for the
development of weak labels in addition to providing a manual annotation
experience. Our tool provides users with multiple user-friendly interfaces for
creating weak labels. SciAnnotate additionally allows users to incorporate
their own language models and visualize the output of their model for
evaluation. In this study, we take multi-source weak label denoising as an
example, we utilized a Bertifying Conditional Hidden Markov Model to denoise
the weak label generated by our tool. We also evaluate our annotation tool
against the dataset provided by Mysore which contains 230 annotated materials
synthesis procedures. The results shows that a 53.7% reduction in annotation
time obtained AND a 1.6\% increase in recall using weak label denoising. Online
demo is available at https://sciannotate.azurewebsites.net/(demo account can be
found in README), but we don't host a model server with it, please check the
README in supplementary material for model server usage.
