Abstract：Event extraction aims at detecting certain specified types of events that are mentioned in the source language data. Existing methods based on supervised learning often suffer from date sparseness and imbalanced distribution, producing low recall as a reuslt. In this paper, we investigate the frame semantic knowledge to improve event extraction. Taking the frame type as general feature and mapping the frames into events, we combine the event recognition model with the frame recognition model for a joint decision. Compared to the previous event recognition model, experiments show that this method achieves 6.44%(5.74%) gain in recall and 1.45%(0.83%) gain in F1 for the task of trigger (event) identification.
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2017, Vol. 31 
