School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan 250358, China.
J Biomed Inform. 2020 Nov;111:103583. doi: 10.1016/j.jbi.2020.103583. Epub 2020 Sep 30.
In recent years, named entity recognition (NER) has attracted significant attention in various fields, especially in the clinical medical field, because NER is essential for useful mining knowledge in the clinical medical area. However, there are still some problems in Chinese named entity recognition, such as the complexity of medical texts, word segmentation errors, and incomplete extraction of semantic information. In this paper, we propose a Chinese NER method based on the multi-granularity semantic dictionary and multimodal tree method, which involves the following steps. First, we extract different semantic words using multimodal trees. Next, we extract the boundary information, and finally, perform the multi-granularity feature fusion. Furthermore, we combine the above methods to complete the entity recognition task. From the results of our experimental verification, our proposed model outperforms the current state-of-the-art results.
近年来，命名实体识别（NER）在各个领域引起了广泛关注，特别是在临床医疗领域，因为 NER 对于从临床医疗领域中有用的知识挖掘至关重要。然而，在中文命名实体识别中仍然存在一些问题，例如医疗文本的复杂性、分词错误和语义信息提取不完整等。在本文中，我们提出了一种基于多粒度语义词典和多模态树方法的中文 NER 方法，该方法涉及以下步骤。首先，我们使用多模态树提取不同的语义词。接下来，我们提取边界信息，最后进行多粒度特征融合。此外，我们结合以上方法来完成实体识别任务。通过实验验证的结果，我们提出的模型优于当前最先进的结果。
