{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:54:36Z","timestamp":1770026076306,"version":"3.49.0"},"reference-count":5,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,3,2]]},"abstract":"<jats:p>Correlation analysis of law-related news is a task to of dividing news into law-related or law-unrelated news, which is the basis of public opinion analysis. Public opinion news consists of the title and the body. The title describes the theme of the news, and the body describes the content of the news. They are equally important and interdependent in the analysis of lawrelated news. Therefore, we make full use of the dependence between the title and the body and propose a learning method that combines the bidirectional attention flow of the title and the body. This method encodes the title and the body respectively by using a bidirectional gated recurrent unit (BiGRU) to obtain the word-level feature matrix of the title and the word-level feature matrix of the body. Then it further extracts the law relevant key features from the body feature matrix, to obtain the word-level feature representation of the body. Finally, we combine the word-level feature representation of the title and the body to build bidirectional attention flow. In this way, the information of the two is fully integrated and interacted to improve the accuracy of the legal correlation analysis of news. To verify the validity of the method in this paper, we conducted experiments on the analysis of law-related news. The results show that our method has achieved good results. Compared with the baseline method, the F1 values of our method is increased by 2.2%, which strongly proves that the interaction between title and body has a good supporting effect on news text classification.<\/jats:p>","DOI":"10.3233\/jifs-201162","type":"journal-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T22:01:42Z","timestamp":1607464902000},"page":"5623-5635","source":"Crossref","is-referenced-by-count":4,"title":["Correlation analysis of law-related news combining bidirectional attention flow of\u00a0news title and body"],"prefix":"10.1177","volume":"40","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Cunli","family":"Mao","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Yuxin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Shengxiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-201162_ref1","first-page":"3110","article-title":"Investigating Capsule Networks with Dynamic Routing for Text Classification, In","volume":"2018","author":"Zhao","journal-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"},{"key":"10.3233\/JIFS-201162_ref11","first-page":"649","article-title":"Recurrent convolutional neural networks for text classification, In","volume":"333","author":"Siwei","year":"2015","journal-title":"Proceedings of AAAI Conference on Artifificial Intelligence. 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In","volume":"33","author":"Yao","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"10.3233\/JIFS-201162_ref19","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2015","journal-title":"Journal of Machine Learning Research"},{"issue":"Suppl.(2)","key":"10.3233\/JIFS-201162_ref20","first-page":"61","article-title":"ResLCNN model for short text classification","volume":"28","author":"Junli","year":"2017","journal-title":"Journal of Software"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-201162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:52:53Z","timestamp":1769993573000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-201162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":5,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-201162","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]}}}