Paper and Patent Data Fusion Based on Deep Text Clustering

被引：0
|
作者：
Xie S. [1 ,2 ]
Wang X. [1 ]
机构：
[1] Institutes of Science and Development, Chinese Academy of Sciences, Beijing
[2] School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing
来源：
Data Analysis and Knowledge Discovery | 2024年 / 8卷 / 04期
关键词：
Data Fusion; Deep Text Clustering; Papers; Patents; Research Topic Identification;
D O I：
10.11925/infotech.2096-3467.2023.0232
中图分类号：
学科分类号：
摘要：
[Objective] This study integrates papers and patents based on research topics to bridge their language gaps. [Method] Using Wikipedia as the primary classification system, we constructed a small number of annotation sets semi-automatically. Then, we designed a semi-supervised deep text clustering model to fuse papers and patents with similar topics. Finally, we created indicators to evaluate the data fusion quality. [Results] Our model’s clustering accuracy was 2.4~11.9% higher than that of other baseline models. Its quality evaluation score of data fusion reached 0.9, which can supplement research topics based on the known topics. [Limitations] We did not conduct empirical analysis using the fused data and need to determine the cluster numbers manually. [Conclusion] The proposed model can extract topic-related features from differentiated texts of papers and patents to effectively realize data fusion. © 2024 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：112 / 124
页数：12
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