In this paper, we aim to minimize the number of hand-tagged training instances needed to start the learning process by adopting a bootstrapping strategy such as that proposed in Riloff and Jones (1999).
to the existing work, we propose a weakly supervised IE framework which takes advantages of both soft and hard matching pattern rules in both the training and test phases.