Automatic Extraction and Visualization of Interaction Networks for German Fairy Tales - David Schmidt; Frank Puppe (Machine Learning and Knowledge Extraction)
Interaction networks are a method of displaying the significant characters in a narrative text and their interactions. We automatically construct interaction networks from dialogues in German fairy tales by the Brothers Grimm and subsequently visualize these networks. This requires the combination of algorithms for several tasks: coreference resolution for the identification of characters and their appearances, as well as speaker/addressee detection and the detection of dialogue boundaries for the identification of interactions. After an evaluation of the individual algorithms, the predicted networks are evaluated against benchmarks established by networks based on manually annotated coreference and speaker/addressee information. The evaluation focuses on specific components of the predicted networks, such as the nodes, as well as the overall network, employing a newly devised score. This is followed by an analysis of various types of errors that the algorithms can make, like a coreference resolution algorithm not realizing that the frog has transformed into a prince, and their impact on the created networks. We find that the quality of many predicted networks is satisfactory for use cases in which the reliability of edges and character types are not of critical importance. However, there is considerable room for improvement.