Incorporating multi-kernel function and Internet verification for Chinese person name disambiguation

Ruifeng Xu, Lin Gui, Qin Lu, Shuai Wang, Jian Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The study on person name disambiguation aims to identify different entities with the same person name through document linking to different entities. The traditional disambiguation approach makes use of words in documents as features to distinguish different entities. Due to the lack of use of word order as a feature and the limited use of external knowledge, the traditional approach has performance limitations. This paper presents an approach for named entity disambiguation through entity linking based on a multikernel function and Internet verification to improve Chinese person name disambiguation. The proposed approach extends a linear kernel that uses in-document word features by adding a string kernel to construct a multi-kernel function. This multi-kernel can then calculate the similarities between an input document and the entity descriptions in a named person knowledge base to form a ranked list of candidates to different entities. Furthermore, Internet search results based on keywords extracted from the input document and entity descriptions in the knowledge base are used to train classifiers for verification. The evaluations on CIPS-SIGHAN 2012 person name disambiguation bakeoff dataset show that the use of word orders and Internet knowledge through a multi-kernel function can improve both precision and recall and our system has achieved state-of-the-art performance.
Original languageEnglish
Pages (from-to)1026-1038
Number of pages13
JournalFrontiers of Computer Science
Volume10
Issue number6
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Chinese person name disambiguation
  • Internet verification
  • machine learning
  • multi-kernel function
  • string kernel

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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