LAUSR

Event detection is the process of analyzing event streams to detect the occurrences of events and categorize them. General methods for solving this problem are to identify and classify event triggers. Most previous works focused on improving the recognition and classification networks which neglected the representation of polysemous event triggers. Polysemy is habitually somewhat confusing in semantic understanding and hard to detect. To improve polysemous trigger detection, this paper proposes a novel framework called DPNPED, which dynamically adjusts the network depth between polysemous and common words. Firstly, to measure the polysemy, the difficulty factor is devised based on the frequency of a word as an event trigger. Secondly, the DPNPED utilizes a confidence measure to automatically adjust the network depth by comparing the predicted and initial probability distribution. Finally, our model applies focal loss to dynamically integrate the difficulty factor and confidence measure to enhance the learning of polysemous triggers. The experimental results show that our method achieves a noticeable improvement in polysemous event trigger detection.