Deep Structured Neural Network for Event Temporal Relation Extraction
Table 1: Data Overview
['[EMPTY]', 'TB-Dense', 'MATRES', 'TCR']
Note that in all three datasets, event pairs are always annotated by their appearance order in text, i.e. given a labeled event pair (i, j), event i always appears prior to event j in the text. Following Meng et al. , we augment event pairs with flipped-order pairs. That is, if a pair (i, j) exists, pair (j, i) is also added to our dataset with the opposite label. The authors project events on multiple axes and only keep those in the main-axis. Start-point temporal scheme is adopted when out-sourcing the annotation task, which contributes to the performance improvement of machine learning models built on this dataset .