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The task of event extraction has long been investigated in a supervisedlearning paradigm, which is bound by the number and the quality of the traininginstances. Existing training data must be manually generated through acombination of expert domain knowledge and extensive human involvement.However, due to drastic efforts required in annotating text, the resultantdatasets are usually small, which severally affects the quality of the learnedmodel, making it hard to generalize. Our work develops an automatic approachfor generating training data for event extraction. Our approach allows us toscale up event extraction training instances from thousands to hundreds ofthousands, and it does this at a much lower cost than a manual approach. Weachieve this by employing distant supervision to automatically create eventannotations from unlabelled text using existing structured knowledge bases ortables.We then develop a neural network model with post inference to transferthe knowledge extracted from structured knowledge bases to automaticallyannotate typed events with corresponding arguments in text.We evaluate ourapproach by using the knowledge extracted from Freebase to label texts fromWikipedia articles. Experimental results show that our approach can generate alarge number of high quality training instances. We show that this large volumeof training data not only leads to a better event extractor, but also allows usto detect multiple typed events.

Scale Up Event Extraction Learning via Automatic Training Data Generation