JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning |
| 영문제목(English Title) |
Case-Related News Filtering via Topic-Enhanced Positive-Unlabeled Learning |
| 저자(Author) |
Guanwen Wang
Zhengtao Yu
Yantuan Xian
Yu Zhang
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| 원문수록처(Citation) |
VOL 17 NO. 6 PP. 1057 ~ 1070 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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. |
| 키워드(Keyword) |
Case-Related News
Iterative Training
Positive-Unlabeled Learning
Topic
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| 파일첨부 |
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