Unsupervised Person Slot Filling based on Graph Mining

Slot filling aims to extract the values (slot fillers) of specific attributes (slots types) for a given entity (query) from a large-scale corpus. Slot filling remains very challenging over the past seven years. 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. Thus 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. Our approach achieves 11.6%-25% higher F-score over state-of-the-art English slot filling methods. Our experiments also demonstrate that as long as a few trigger seeds, name tagging and dependency parsing capabilities exist, this approach can be quickly adapted to any language and new slot types. Our promising results on Chinese slot filling can serve as a new benchmark.