  The task of event extraction has long been investigated in a supervised
learning paradigm, which is bound by the number and the quality of the training
instances. Existing training data must be manually generated through a
combination of expert domain knowledge and extensive human involvement.
However, due to drastic efforts required in annotating text, the resultant
datasets are usually small, which severally affects the quality of the learned
model, making it hard to generalize. Our work develops an automatic approach
for generating training data for event extraction. Our approach allows us to
scale up event extraction training instances from thousands to hundreds of
thousands, and it does this at a much lower cost than a manual approach. We
achieve this by employing distant supervision to automatically create event
annotations from unlabelled text using existing structured knowledge bases or
tables.We then develop a neural network model with post inference to transfer
the knowledge extracted from structured knowledge bases to automatically
annotate typed events with corresponding arguments in text.We evaluate our
approach by using the knowledge extracted from Freebase to label texts from
Wikipedia articles. Experimental results show that our approach can generate a
large number of high quality training instances. We show that this large volume
of training data not only leads to a better event extractor, but also allows us
to detect multiple typed events.
