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Unsupervised Slot Filler Refinement via Entity Community Construction

  • Zengzhuang Xu
  • Rui Song
  • Bowei ZouEmail author
  • Yu Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10619)

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.

Notes

Acknowledgement

This research work is supported by National Natural Science Foundation of China (Grants No. 61672367, No. 61672368, No. 61703293), the Research Foundation of the Ministry of Education and China Mobile, MCM20150602 and the Science and Technology Plan of Jiangsu, SBK2015022101 and BK20151222. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Soochow UniversitySuzhouChina

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