Visualization for Coreference Annotation

The annotation of documents with linguistic information requires time-consuming and therefore expensive manual annotation. Especially, a complex task, like coreference resolution, needs large data sets for the training of supervised machine learning methods. We present a tool which combines visualization techniques and unsupervised machine learning to support the annotation of documents with coreference information. Self-organizing Maps are used to cluster similar data and visualize the feature space. For link visualization, precise annotation, and error correction a matrix-based coreference visualization is used which exploits the transitive property of the coreference relation.