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| Research on Constructing a Model of Correlation Discrimination Between Funds and Funded Papers Based on Siamese Network |
| Ye Wenhao1,2, Wang Dongbo3, Shen Si4, Su Xinning1,2 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023
3.College of Information and Technology, Nanjing Agricultural University, Nanjing 210095
4.School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094 |
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Abstract To explore the phenomenon of mislabeling fund projects in research papers, this study proposes a deep learning model to calculate the correlation between the fund and its sponsored paper. Considering the National Social Science Fund Project and its sponsored papers as the data source, the similarity between the fund title and the title and abstract of the paper is calculated based on the word2vec model. The correlation score of text similarity establishes that there are differences between the fund content and its sponsored papers. By manually reviewing the low-similarity data pairs, we confirm that some funds are mislabeled. Finally, the correlation model between the fund and its sponsored papers is developed. This model is effective in detecting the papers with mislabeled fund projects with a precision of over 99%. The recall and F-score of the model that uses Transformer as the encoder are estimated at 89.13% and 94.22%, respectively. This model can aid in suppressing the fund s mislabeling behavior effectively from both author submissions and journal reviews.
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Received: 23 July 2019
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