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Cross-Lingual Type Inference

  • Bo Xu
  • Yi Zhang
  • Jiaqing Liang
  • Yanghua XiaoEmail author
  • Seung-won Hwang
  • Wei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)

Abstract

Entity typing is an essential task for constructing a knowledge base. However, many non-English knowledge bases fail to type their entities due to the absence of a reasonable local hierarchical taxonomy. Since constructing a widely accepted taxonomy is a hard problem, we propose to type these non-English entities with some widely accepted taxonomies in English, such as DBpedia, Yago and Freebase. We define this problem as cross-lingual type inference. In this paper, we present CUTE to type Chinese entities with DBpedia types. First we exploit the cross-lingual entity linking between Chinese and English entities to construct the training data. Then we propose a multi-label hierarchical classification algorithm to type these Chinese entities. Experimental results show the effectiveness and efficiency of our method.

Keywords

Entity Typing Name Entity Recognition Local Knowledge Base Head Compound English Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bo Xu
    • 1
  • Yi Zhang
    • 1
  • Jiaqing Liang
    • 1
  • Yanghua Xiao
    • 1
    • 2
    Email author
  • Seung-won Hwang
    • 3
  • Wei Wang
    • 1
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Internet Big Data Engineering and Technology CenterShanghaiChina
  3. 3.Department of Computer ScienceYonsei UniversitySeoulSouth Korea

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