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arXiv · 2208.10241

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

Mengyang Liu·Haozheng Luo·Leonard Thong·Yinghao Li·Chao Zhang·Le Song
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Abstract

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.

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Mengyang Liu, Haozheng Luo, Leonard Thong, Yinghao Li, Chao Zhang, Le Song. 2022-08-07. SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling. https://arxiv.org/abs/2208.10241

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