Abstract
Thesis classification is fundamental to a wide range of efficient research management. Current thesis classification is limited to major, research direction and classification number manually labeled by students themselves, which lacks standard and accuracy. Furthermore, previous auto-classification studies do not take account of interdisciplinary. This study intends to make a major contribution to Chinese thesis classification by taking advantage of the metadata such as title, keywords in the thesis. We propose a novel hierarchical classification model based on methods in metadata semantic representation and the corresponding similarity calculation. Experiments on 4K+ Theses show our methods have significant effect.
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Notes
- 1.
The version we are using is GB/T13745-2009. This taxonomic hierarchy is divided into three levels: first-level disciplines, second-level disciplines, and third-level disciplines.
- 2.
Wanfang Data is one of the most popular knowledge service platforms in China.
- 3.
We define the title and keywords of a thesis as its central words.
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Acknowledgements
The research is supported by the National Key Research and Development Program of China (2018YFB1004502) and the National Natural Science Foundation of China (61532001, 61303190).
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Li, J., Yu, S., Li, S., Yu, J. (2019). Multi-classification of Theses to Disciplines Based on Metadata. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_43
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DOI: https://doi.org/10.1007/978-3-030-32236-6_43
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