Knowledge-aware Few Shot Learning for Event Detection from Short Texts

Event detection in a city is crucial for the government to listen to the voice of the citizens, be aware of the real occurrences in a city, and then make wiser policies. However, in reality some important events with few samples are easily to be overwhelmed by the massive information and hard to be recognized, and additionally the limited word description from the short texts even makes the recognition harder. To address the problems, we propose a knowledge-aware event detector by incorporating the external knowledge to detect the events with few examples. The external knowledge incorporation with different semantic relations is capable to enrich the short texts. In addition, we leverage the representative few shot learning framework to formulate the event detection as the text classification problem. The proposed model is evaluated on two widely event-detection datasets. The experiments show a consistent accuracy improvement. The findings validates that our model with the knowledge infusion is effective to detect the few shot events from the short texts.