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Research on Unsupervised Cross-language Patent Recommendation Based on Representation Learning
Zhang Jingzhu,Zhu Lipeng,Liu Jingjie
( School of Economics and Management, Nanjing University of Science and Technology, Nanjing  210094)
( Jiangsu Provincial Social Public Safety Science and Technology Collaborative Innovation Center, Nanjing  210094)
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[Objective] The object is to reduce the construction of bilingual dictionaries and large-scale corpus, improve the effect of cross-language patent recommendation and the ability of domain adaptation, from the perspective of patent text semantic representation. [Methods] Firstly, the method of unsupervised cross-language word vector mapping is designed, and the independent Chinese and English word vector is mapped to the unified semantic vector space by linear transformation, to construct the semantic mapping relationship between Chinese and English words. Then, the method of semantic representation of patent text based on cross-language word vector is formed with smooth inverse frequency (SIF) reweighting method, to realize the semantic representation of Chinese-English patent text in the same vector space. Finally, the vector similarity calculation method was used to calculate the semantic similarity between patent texts in different languages. [Results] Experiments related to "wireless communication" show that this method can achieve comprehensive and accurate Chinese-English cross-language patents recommendation. The recommendation accuracy rate of the top 1 and the top 5 reached 55.63% and 77.82%, which has increased by 0.66% and 1.45% to the weak supervised based cross-language recommendation and 4.29% and 3.9% to the machine translation based cross-language recommendation, respectively. [Limitations] Only Chinese and English patents are recommended in specific fields, so the fields and language scopes need to be expanded. [Conclusions] This method can be expanded to the research and application of patents recommendation in other domains and languages.

Key words cross-language      patent recommendation      representation learning      text semantics      
Published: 28 July 2020
ZTFLH:  G254  

Cite this article:

Zhang Jingzhu, Zhu Lipeng, Liu Jingjie. Research on Unsupervised Cross-language Patent Recommendation Based on Representation Learning . Data Analysis and Knowledge Discovery, 0, (): 1-.

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