Leveraging Approximate Pattern Matching with BERT for Event Detection

AbstractWe describe a new weakly supervised method for sentence-level event detection, based exclusively on linear prototype patterns like “people got sick” or “a roadside bomb killed people”. We propose a new BERT based algorithm for approximate pattern matching to identify event phrases, semantically similar to these prototypes. To the best of our knowledge, a similar approach has not been used in the context of event detection. We experimented with two event corpora in the area of disease outbreaks and terrorism and we achieved promising results in sentence level event identification: 0.78 F1 score for new disease cases detection and 0.68 F1 in detecting terrorist attacks. Results were in line with some state-of-the-art systems.