Text Segmentation and Graph-based Method for Template Filling in Information Extraction

In event-based Information Extraction systems, a major task is the automated filling from unstructured texts of a template gathering information related to a particular event. Such template filling may be a hard task when the information is scattered throughout the text and mixed with similar pieces of information relative to a different event. We propose in this paper a twostep approach for template filling: first, an event-based segmentation is performed to select the parts of the text related to the target event; then, a graph-based method is applied to choose the most relevant entities in these parts for characterizing the event. An evaluation of this model based on an annotated corpus for earthquake events shows that we achieve a 77% F1-measure for the template-filling task.