Journal of Information Processing Systems

  • 제17권6호
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  • Pages.1057-1070
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  • 2021
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning

  • Wang, Guanwen (College of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Yu, Zhengtao (Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Xian, Yantuan (College of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Zhang, Yu (College of Information Engineering and Automation, Kunming University of Science and Technology)
  • 투고 : 2021.01.22
  • 심사 : 2021.05.30
  • 발행 : 2021.12.31
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초록

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

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과제정보

This study was supported by the project of the National Key Research and Development Project (No. 2018YFC0830100) and the Science and Technology Plan Projects of Yunnan province (No. 202001AT070046).

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