RealTime Collaborative Annotation System Supporting Separation of Content and Annotation In earlier collaborative annotation system, the details of all comments were presented in the form of images and were marked directly on the contents. This approach may cause visual confusion no matter what style people choose to display annotations by overlaying contents or by overlapping other comments. This article introduces a new annotation display model to separate contents and annotations, and the MPSAC (Multiprocessing and Separation of Annotation and Content) strategy was presented to achieve the consistency maintenance of different collaborative sites based on this model. With the foundation of controlling executive operationsxe2x80x99 effect and maintaining the consistency of annotations, the strategy discovers and resolves the collision problem among overlapped annotations and provide a better interactive experience for users. The feasibility and correctness of this strategy were verified by case analysis and CoNote model system at the end of this paper.

VIANA: Visual Interactive Annotation of Argumentation Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on timeconsuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a longterm collaboration with domain experts, we identify and model five highlevel analysis tasks. We enable close reading and notetaking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text and graphbased layers. We evaluate our system with a twostage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.