Event Detection with Entity Markers

Emanuela Boros, Jose G. Moreno, Antoine Doucet

Abstract: Event detection involves the identification of instances of specified types of events in text and their classification into event types. In this paper, we approach the event detection task as a relation extraction task. In this context, we assume that the clues brought by the entities participating in an event are important and could improve the performance of event detection. Therefore, we propose to exploit entity information explicitly for detecting the event triggers by marking them at different levels while fine-tuning a pre-trained language model. The experimental results prove that our approach obtains state-of-the-art results on the ACE 2005 dataset.