{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:47:19Z","timestamp":1781279239693,"version":"3.54.1"},"reference-count":15,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>The rapid development of the Internet has led to a geometric expansion of text information resources online. Among them, corpus, as the basic data source of natural language processing based on statistical language model, its construction and application have become a hot issue in current language processing research. After consulting a large number of relevant literature and materials, it was found that many researchers have provided new ideas for multi label corpus text classification methods. However, this article adds its own understanding and takes this as the direction and basis. In the introduction, the research significance of text classification was introduced, and then academic research and analysis were carried out on the two key sentences of corpus text classification and natural language processing in multi-tag corpus text classification. This article then utilizes an algorithm model to provide a theoretical basis for the study of multi-label corpus text classification methods; At the end of this article, a simulation comparative experiment is conducted, and the experiment is summarized and discussed; In the Enterprise L corpus, the difference in recall rates before and after the use of Entrance 1 was 5.5%, the difference in recall rates before and after the use of Entrance 2 was 7.8%, the difference in recall rates before and after the use of Entrance 3 was 3.3%, and the difference in recall rates before and after the use of Entrance 4 was 4.5%. At the same time, with the continuous research of natural language processing and machine learning, the research on text classification methods of multi tag corpus is also facing new opportunities and challenges.<\/jats:p>","DOI":"10.1145\/3617831","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T11:22:44Z","timestamp":1693308164000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Text Classification Based on Natural Language Processing and Machine Learning in Multi-Label Corpus"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6546-0013","authenticated-orcid":false,"given":"Haitao","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7961-3080","authenticated-orcid":false,"given":"Feng","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8984-5139","authenticated-orcid":false,"given":"Zuhui","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Education, University of Hong Kong, Hong Kong 999077, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.08.001"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2020.8"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/1094428117719322"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-021-00679-x"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2018.06.004"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3298-8"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.30630\/joiv.3.4.289"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.15837\/ijccc.2018.1.3142"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439726"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11625-022-01093-3"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3093065"},{"issue":"3","key":"e_1_3_1_13_2","first-page":"31","article-title":"Clinical text classification with rule-based features and knowledge-guided convolutional neural networks","volume":"19","author":"Yao Liang","year":"2019","unstructured":"Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. 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