Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 206-211.doi: 10.11896/jsjkx.210900120

• Artificial Intelligence • Previous Articles     Next Articles

Relation Extraction Based on Multidimensional Semantic Mapping

CHENG Hua-ling, CHEN Yan-ping, YANG Wei-zhe, QIN Yong-bin, HUANG Rui-zhang   

  1. College of Computer Science and Technology,Guizhou University,Guiyang,550025,China
    State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
  • Received:2021-09-14 Revised:2022-04-18 Online:2022-11-15 Published:2022-11-03
  • About author:CHENG Hua-ling,born in 1996,postgraduate.Her main research interests include natural language processing and relation extraction.
    QIN Yong-bin,born in 1980,Ph.D,professor.His main research interests include big data governance and application,and multi-source data fusion.
  • Supported by:
    State Key Program of the Joint Funds of National Natural Science of China(U1836205),National Natural Science Foundation of China(62066007,62066008) and Key Project of Guizhou Science and Technology Fund(Qian Ke He Ji Chu[2020]1Z055).

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Abstract: Relation extraction aims to identify relation types between entities from texts.In the field of relation extraction,most of existing methods use deep learning methods,but they do not have in-depth discussion of word vectors in the input layer.To further exploit word vectors,this paper proposes a relation extraction method based on multi-dimensional semantic mapping.The core idea of the method is to reduce dimensionality of text feature matrix before the word vector enters the input layer.Experimental results show that the proposed method not only can reduce dimensionality effectively,but also can represent the semantic information of the same sentence in different dimensions,with its F1 of 75.3% and 88.9% on the Chinese Literature Text and SemEval-2010 Task8 datasets,respectively.

Key words: Relation extraction, Neural network, Multidimensional mapping, Semantic information

CLC Number: 

  • TP391
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