Event Detection Model Integrating Part of Speech Semantic Extension Information
Event detection is one of the key steps in event extraction,which depends on the identified triggers for event type classification.Current mainstream event detection methods exhibit poor performance on sparsely labeled data,which overfit the model with densely labeled triggers and fail on the sparsely labeled or unseen triggers.Most previous methods mitigate this problem by adding more training examples;however,the expanded data are distributed unevenly,have built-in biases,and still perform poorly.To this end,this study explores word granularity expansion information to mitigate the impact of the problem of sparsely labeled data by reducing the range of candidate triggers,and mining the rich semantic information in the contexts without increasing the number of training instances.First,a part of speech selection module is applied to find candidate triggers and extend their semantics,which digs out word granularity semantic information.Thereafter,sentence granularity semantic information is incorporated to improve the robustness of semantic information.Finally,event types classification is performed by Softmax function,which completes the event detection task.Experimental results on ACE2005 and KBP2015 datasets demonstrate that the model achieves F1 scores of 79.5%and 67.5%in the event detection task,respectively,effectively improving the performance of event detection.The F1 score reaches 78.5%in the sparsely labeled data experiments,thereby alleviating the sparsely labeled data problem significantly.
event detectionsparse labelpart of speech filteringsemantic extensionsemantic integrationdynamic multi-pooling
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