We propose a simple yet effective unsupervised approach to extract slot fillers based on the following two observations: (1) a trigger is usually a salient node relative to the query and filler nodes in the dependency graph of a context sentence; (2) a relation is likely to exist if the query and candidate filler nodes are strongly connected by a relation-specific trigger.
we design a graph-based algorithm to automatically identify triggers based on personalized PageRank and Affinity Propagation for a given (query, filler) pair and then label the slot type based on the identified triggers.