Unsupervised slot filler refinement via entity community construction

Abstract

Given an entity (query), slot filling aims to find and extract the values (slot fillers) of its specific attributes (slot types) from a large-scale of document collections. Most existing work of slot filling models slot fillers separately and only considers direct relations between slot fillers and query, ignoring other slot fillers in context. In this paper we propose an unsupervised slot filler refinement approach via entity community construction to filter out the incorrect fillers collaboratively. The community-based framework mainly consists of (1) filler community generated by a point-wise mutual information-based hierarchical clustering, and (2) query community constructed by a co-occurrence graph model.