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https://ir.lib.ncu.edu.tw/handle/987654321/106420
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| 題名: | Boosted web named entity recognition via tri-training |
| 作者: | 張嘉惠;Chou, Chien-Lung;Chang, Chia-Hui;Huang, Ya-Yun |
| 貢獻者: | 資訊電機學院資訊工程學系 |
| 日期: | 2016-10-01 |
| 上傳時間: | 2026-04-23 13:22:09 (UTC+8) |
| 出版者: | Association for Computing Machinery (ACM) |
| 摘要: | 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 |
| 顯示於類別: | [資訊工程學系] 期刊論文
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