Boosted web named entity recognition via tri-training

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106420

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106420

Title:	Boosted web named entity recognition via tri-training
Authors:	張嘉惠;Chou, Chien-Lung;Chang, Chia-Hui;Huang, Ya-Yun
Contributors:	資訊電機學院資訊工程學系
Date:	2016-10-01
Issue Date:	2026-04-23 13:22:09 (UTC+8)
Publisher:	Association for Computing Machinery (ACM)
Abstract:	摘要： 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. 出版日期： 2017-06-30 出處： ACM transactions on Asian and low-resource language information processing, 2017-06, Vol.16 (2), p.1-23 資源來源： ACM Digital Library Complete 識別號： ISSN: 2375-4699 識別號： EISSN: 2375-4702 識別號： DOI: 10.1145/2963100
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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