Improving Event Detection by Automatically Assessing Validity of Event Occurrence in Text

Manually inspecting text to assess whether an event occurs in a document collection is an onerous and time consuming task. Although a manual inspection to discard the false events would increase the precision of automatically detected sets of events, it is not a scalable approach. In this paper, we automatize event validation, defined as the task of determining whether a given event occurs in a given document or corpus. The introduction of automatic event validation as a post-processing step of event detection can boost the precision of the detected event set, discarding false events and preserving the true ones. We propose a novel automatic method for event validation, which relies on a supervised model to predict the occurrence of events in a non-annotated corpus. The data for training the model is gathered by exploiting the crowdsourcing paradigm. Experiments on real-world events and documents show that our proposed method (i) outperforms the state-of-the-art event validation approach and (ii) increases the precision of event detection while preserving recall.