Cascading use of soft and hard matching pattern rules for weakly supervised information extraction

Current rule induction techniques based on hard matching (i.e., strict slot-by-slot matching) tend to fare poorly in extracting information from natural language texts, which often exhibit great variations. The reason is that hard matching techniques result in relatively high precision but low recall. To tackle this problem, we take advantage of the newly proposed soft pattern rules which offer high recall through the use of probabilistic matching. We propose a bootstrapping framework in which soft and hard matching pattern rules are combined in a cascading manner to realize a weakly supervised rule induction scheme. The system starts with a small set of hand-tagged instances. At each iteration, we first generate soft pattern rules and utilize them to tag new training instances automatically. We then apply hard pattern rule induction on the overall tagged data to generate more precise rules, which are used to tag the data again. The process can be repeated until satisfactory results are obtained. Our experimental results show that our bootstrapping scheme with two cascaded learners approaches the performance of a fully supervised information extraction system while using much fewer hand-tagged instances.