Event-based Time Label Propagation for Automatic Dating of News Articles

Since many applications such as timeline sum-maries and temporal IR involving temporal analysis rely on document timestamps, the task of automatic dating of documents has been increasingly important. Instead of using feature-based methods as conventional mod-els, our method attempts to date documents in a year level by exploiting relative tempo-ral relations between documents and events, which are very effective for dating documents. Based on this intuition, we proposed an event-based time label propagation model called confidence boosting in which time label in-formation can be propagated between docu-ments and events on a bipartite graph. The ex-periments show that our event-based propaga-tion model can predict document timestamps in high accuracy and the model combined with a MaxEnt classifier outperforms the state-of-the-art method for this task especially when the size of the training set is small.