Algorithm Research & Explore
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2023-2026

Sentence vector based on semantic relationship constraints and word relationship information

Xia Xiaoqiang
Shao Kun
School of Computer Science & Information, Hefei University of Technology, Hefei 230009, China

Abstract

In view of the fact that the existing sentence vector learning method can not well learn the relational knowledge information and express the complicated semantic relation, this paper proposed a relational information sentence vector model(RISV) based on the PV-DM model and the relational information model. This model used the PV-DM model as the basic model of sentence vector training, and then added the knowledge constraint of relational information to make the improved model could learn the relationship between the words in the text and used the RCM model as pre-training model to further integrate the information of the semantic relationship constraints, and finally validated the validity of the RISV model in two tasks: document classification and short text semantic similarity. The experimental results show that sentence vectors learned by RISV model can better represent the text.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.01.0029
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 7
Section: Algorithm Research & Explore
Pages: 2023-2026
Serial Number: 1001-3695(2019)07-024-2023-04

Publish History

[2019-07-05] Printed Article

Cite This Article

夏小强, 邵堃. 基于语义关系约束和词语关系信息的句向量研究 [J]. 计算机应用研究, 2019, 36 (7): 2023-2026. (Xia Xiaoqiang, Shao Kun. Sentence vector based on semantic relationship constraints and word relationship information [J]. Application Research of Computers, 2019, 36 (7): 2023-2026. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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