【研究發展處訊】

巨量資料管理學院蔡芸琤助理教授發表最新期刊論文

Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning

作者：Hung-Kai Kung, Chun-Mo Hsieh, Cheng-Yu Ho, Yun-Cheng Tsai*, Hao-Yung Chan and Meng-Han Tsai

Applied Sciences (SCI)

卷數：10

期數：12

頁碼：4234

出版日期：20 June 2020

摘要：

This research aims to build a Mandarin named entity recognition (NER) module using transfer learning to facilitate damage information gathering and analysis in disaster management. The hybrid NER approach proposed in this research includes three modules: (1) data augmentation, which constructs a concise data set for disaster management; (2) reference model, which utilizes the bidirectional long short-term memory–conditional random field framework to implement NER; and (3) the augmented model built by integrating the first two modules via cross-domain transfer with disparate label sets. Through the combination of established rules and learned sentence patterns, the hybrid approach performs well in NER tasks for disaster management and recognizes unfamiliar words successfully. This research applied the proposed NER module to disaster management. In the application, we favorably handled the NER tasks of our related work and achieved our desired outcomes. Through proper transfer, the results of this work can be extended to other fields and consequently bring valuable advantages in diverse applications.

研究事務組小提醒：教師如有最新發表於AHCI、SSCI、SCI、EI、TSSCI、THCI、「東吳大學外語學門獎勵名單」之期刊論文，歡迎將相關資訊e-mail至rad@scu.edu.tw，研究發展處將會公告於校園頭條，以廣交流。

【文圖/研究事務組游晴如組員】