Learning event templates on news articles
We propose a pipeline for learning event templates from a large corpus of textual news articles. An event template is a machine-usable semantic data structure, in our case a graph, describing a certain event type. For instance, most earthquake news reports mention something in direction of "x people dead” or “town y shook at time z". Such templates can be used as an input for information extraction tasks or automated ontology extension. We present preliminary results of applying the proposed pipeline on a subset of News articles.