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Automatic Identification of Term Citation Object with Feature Fusion |
Na Ma1,2,Zhixiong Zhang1,2,3,4(),Pengmin Wu5 |
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2School of Economic and Management, University of Chinese Academy of Sciences, Beijing 100190, China 3Wuhan Library, Chinese Academy of Sciences, Wuhan 430071, China 4Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071, China 5Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract [Objective] This paper explores methods automatically identifying term citation objects from scientific papers, with feature fusion and pseudo-label noise reduction strategy.[Methods] First, we converted the identification of term citation objects into sequential annotation. Then, we combined linguistic and heuristic features of term citation objects in the BiLSTM-CNN-CRF input layer, which enhanced their feature representations. Finally, we designed pseudo-label learning noise reduction mechanism, and compared the performance of different models.[Results] The optimal F1 value of our method reached 0.6018, which was 8% higher than that of the BERT model.[Limitations] The experimental data was collected from computer science articles, thus, our model needs to be examined with data from other fields.[Conclusions] The proposed method could effectively identify term citation objects.
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Received: 23 July 2019
Published: 14 March 2020
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Corresponding Authors:
Zhixiong Zhang
E-mail: zhangzhx@mail.las.ac.cn
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