ABL-TCM: An Abductive Framework for Named Entity Recognition in Traditional Chinese Medicine - Zongyao Zhao; Yong Tang; Zhitao Cheng; Yanlin Leng; Liling Tang (IEEE Access, 2024)
As a foundation for supporting downstream tasks, the named entity recognition (NER) task in the field of traditional Chinese medicine (TCM) is gaining increasing attention. However, the TCM domain suffers from a lack of labeled data and a high annotation error rate. To solve this problem, we propose an abductive learning framework for named TCM entity recognition called ABL-TCM. First, we design a series of methods to mitigate the negative impact of mislabeling on the effectiveness of the constructed model and to enhance the focus of the training process on challenging entities. Second, we offer a flexible approach for incorporating external unsupervised data. Finally, we propose a label correction mechanism based on abductive learning to ensure that only reliable data are included in the training process. The results of several experiments prove the effectiveness of ABL-TCM, which achieves the best results on a TCM-NER dataset.