CSAA: An Online Annotating Platform for Classifying Sections of Academic Articles

As the basic work of knowledge mining and service based on full-text of articles, recognizing the categories of section in academic articles can help us to understand the function of content in different parts of the article. There is no existing a large-scale annotated corpus of section categories which can be used to classify the sections of the articles via machine learning. This study developed an annotating platform, namely CASS to implement the functions, including grouping annotators, task assignment, online tagging and doubtful corpus review. It can improve the tagging efficiency and provide convenience for task management, data preservation, results review and verifying inconsistent results.