Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000111-6.doi: 10.11896/jsjkx.211000111

• Artificial Intelligence • Previous Articles     Next Articles

Cross-lingual Term Alignment with Kernel-XGBoost

YU Juan, ZHANG Chen   

  1. School of Economics and Management,Fuzhou University,Fuzhou 350108,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:YU Juan,born in 1981,professor,Ph.D supervisor.Her main research interests include data science and knowledge engineering,intelligent information system.
    ZHANG Chen,born in 1997,postgra-duate.Her main research interests include cross-language text analysis and knowledge discovery.
  • Supported by:
    National Natural Science Foundation of China(71771054).

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Abstract: Cross-lingual term alignment is a crucial step for cross-lingual text data analysis and knowledge discovery.Current research usually focuses on single-word term alignment and relies heavily on vector space alignment.Therefore,a new Kernel-XGBoost method is proposed for the one-to-many alignment of cross-lingual terms including multi-word terms.Given a cross-lingual parallel corpus,the proposed method obtains synonymous cross-lingual terms in two steps:1) extracting cross-lingual terms and generating candidate term pairs;2) aligning cross-lingual terms based on word embedding.Experiments on Chinese-Spanish and Chinese-French term alignments demonstrate that the proposed method can achieve an accuracy of 80% at Top-5.It can effectively support cross-lingual text mining tasks such as information retrieval,ontology building.

Key words: Cross-lingual, Text analysis, Term alignment, Kernel-XGBoost, Chinese, French, Spanish

CLC Number: 

  • G202
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