Algorithm 1 Training procedure for NSEM at the time period t Require: main bodies d; titles h; model parameter Φ t−1 at the time period t − 1 1: Initialize Φ t 2: Calculate its storyline distribution based on Φ t−1 4: end for 5: repeat 6: Calculate the storyline distribution Calculate the storyline distribution p(s h i,pos ) Sample an irrelevant title h i,neg where h i,neg ∩ h i,pos = ∅ Calculate the storyline distribution p(s h i,neg ) 12: Update model parameter Φ t end for 18: until Convergence As the number of storylines at each time period is assumed to be the same, some newly emerging storylines might be incorrectly linked with previous storylines.
post-processing is needed to filter out such erroneous linkings.