An annotation framework for dense event ordering

Abstract

Today's event ordering research is heavily dependent on annotated corpora. Current corpora influence shared evaluations and drive algorithm development. Partly due to this dependence, most research focuses on partial orderings of a document's events. For instance, the TempEval competitions and the TimeBank only annotate small portions of the event graph, focusing on the most salient events or on specific types of event pairs (e.g., only events in the same sentence). Deeper temporal reasoners struggle with this sparsity because the entire temporal picture is not represented. This paper proposes a new annotation process with a mechanism to force annotators to label connected graphs. It generates 10 times more relations per document than the TimeBank, and our TimeBank-Dense corpus is larger than all current corpora. We hope this process and its dense corpus encourages research on new global models with deeper reasoning. © 2014 Association for Computational Linguistics.