Discovering and learning sensational episodes of news events

In this paper, we study the problem of discovering and learning sensational episodes of news events. A sensational episode of news events is in the form of lhs → rhs, where lhs is an antecedent event, rhs is a consequent event, and rhs often happens shortly after lhs. Such pairs of co-occurring news events within short period, while not necessarily bearing causal relationship between each other, are possible essential to media since they deliberately seek and broadcast examples of uncommon events to fascinate crowd attentions. First, to find all frequent episodes, we propose an efficient algorithm, MEELO, which significantly outperforms conventional algorithms. There can be a large number of frequent episodes. We rank them by their sensational effect from the perspectives of news audience, through learning from manually labeled examples. Instead of limiting ourselves to any individual subjective measure of sensational effect, we utilize a learning-to-rank approach that exploits multiple features to capture the sensational effect of a news episode from various aspects. NLP tools combined with knowledge bases are used in extracting and aggregating news events from news text. Experiments on real data verify our approach's efficiency and effectiveness.