{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T09:04:39Z","timestamp":1766653479825,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2016,10,14]],"date-time":"2016-10-14T00:00:00Z","timestamp":1476403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Industrial Technology Research Institute of Taiwan","award":["B2-101052"],"award-info":[{"award-number":["B2-101052"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2017,6,30]]},"abstract":"<jats:p>Named entity extraction is a fundamental task for many natural language processing applications on the web. Existing studies rely on annotated training data, which is quite expensive to obtain large datasets, limiting the effectiveness of recognition. In this research, we propose a semisupervised learning approach for web named entity recognition (NER) model construction via automatic labeling and tri-training. The former utilizes structured resources containing known named entities for automatic labeling, while the latter makes use of unlabeled examples to improve the extraction performance. Since this automatically labeled training data may contain noise, a self-testing procedure is used as a follow-up to remove low-confidence annotation and prepare higher-quality training data. Furthermore, we modify tri-training for sequence labeling and derive a proper initialization for large dataset training to improve entity recognition. Finally, we apply this semisupervised learning framework for person name recognition, business organization name recognition, and location name extraction. In the task of Chinese NER, an F-measure of 0.911, 0.849, and 0.845 can be achieved, for person, business organization, and location NER, respectively. The same framework is also applied for English and Japanese business organization name recognition and obtains models with performance of a 0.832 and 0.803 F-measure.<\/jats:p>","DOI":"10.1145\/2963100","type":"journal-article","created":{"date-parts":[[2016,10,14]],"date-time":"2016-10-14T13:34:24Z","timestamp":1476452064000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Boosted Web Named Entity Recognition via Tri-Training"],"prefix":"10.1145","volume":"16","author":[{"given":"Chien-Lung","family":"Chou","sequence":"first","affiliation":[{"name":"National Central University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Hui","family":"Chang","sequence":"additional","affiliation":[{"name":"National Central University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Yun","family":"Huang","sequence":"additional","affiliation":[{"name":"National Central University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,10,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/1075178.1075207"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219841"},{"volume-title":"Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II. 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CRF++: Yet Another CRF toolkit. (2005). http:\/\/crfpp.googlecode.com.  Taku Kudo. 2005. CRF++: Yet Another CRF toolkit. (2005). http:\/\/crfpp.googlecode.com."},{"volume-title":"Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC\u201902)","year":"2002","author":"Lin Jimmy","key":"e_1_2_1_19_1"},{"key":"e_1_2_1_20_1","article-title":"Generalized expectation criteria for semi-supervised learning with weakly labeled data","author":"Mann Gideon S.","year":"2010","journal-title":"Journal of Machine Learning Research 11"},{"volume-title":"Proceedings of the 17th International Conference on Machine Learning (ICML\u201900)","author":"McCallum Andrew","key":"e_1_2_1_21_1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119206"},{"volume-title":"Proceedings of the 21st International Joint Conference on Artifical Intelligence (IJCAI\u201909)","year":"2076","author":"Michelson Matthew","key":"e_1_2_1_23_1"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/1690219.1690287"},{"key":"e_1_2_1_25_1","first-page":"112","article-title":"Using semi-supervised learning for question classification","volume":"3","author":"Nguyen Tri Thanh","year":"2008","journal-title":"Information and Media Technologies"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"volume-title":"Proceedings of Annual Meeting of the Association for Computational Linguistics.","year":"2013","author":"Qiu Xipeng","key":"e_1_2_1_27_1"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348379"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119180"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000003"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/14.212242"},{"key":"e_1_2_1_32_1","first-page":"L","article-title":"Learning syntactic patterns for automatic hypernym discovery","volume":"17","author":"Snow Rion","year":"2005","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_33_1","unstructured":"Yueng-Sheng Su. 2012. 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