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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

Lv, C, Pan, D, Li, Y, Li, Jianxin and Wang, Z 2021, A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model, Complexity, vol. 2021, pp. 1-8, doi: 10.1155/2021/6610965.

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Title A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model
Author(s) Lv, C
Pan, D
Li, Y
Li, JianxinORCID iD for Li, Jianxin orcid.org/0000-0002-9059-330X
Wang, Z
Journal name Complexity
Volume number 2021
Article ID 6610965
Start page 1
End page 8
Total pages 8
Publisher Hindawi
Place of publication London, Eng.
Publication date 2021
ISSN 1076-2787
1099-0526
Summary To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.
Language eng
DOI 10.1155/2021/6610965
Indigenous content off
Field of Research 0102 Applied Mathematics
0103 Numerical and Computational Mathematics
0802 Computation Theory and Mathematics
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148101

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.