文献类型：期刊文献

英文题名：Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts

作者：Ye, Qi[1];Cai, Tingting[1];Ji, Xiang[1];Ruan, Tong[1];Zheng, Hong[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Technol, Shanghai 200237, Peoples R China

年份：2023

卷号：23

期号：1

外文期刊名：BMC MEDICAL INFORMATICS AND DECISION MAKING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000933488600003)】；

语种：英文

外文关键词：Active learning; Sequence tagging; Relation extraction; Distant supervision; Medical texts

摘要：In recent years, relation extraction on unstructured texts has become an important task in medical research. However, relation extraction requires a large amount of labeled corpus, manually annotating sequences is time consuming and expensive. Therefore, efficient and economical methods for annotating sequences are required to ensure the performance of relational extraction. This paper proposes a method of subsequence and distant supervision based active learning. The method is annotated by selecting information-rich subsequences as a sampling unit instead of the full sentences in traditional active learning. Additionally, the method saves the labeled subsequence texts and their corresponding labels in a dictionary which is continuously updated and maintained, and pre-labels the unlabeled set through text matching based on the idea of distant supervision. Finally, the method combines a Chinese-RoBERTa-CRF model for relation extraction in Chinese medical texts. Experimental results test on the CMeIE dataset achieves the best performance compared to existing methods. And the best F1 value obtained between different sampling strategies is 55.96%.