A joint entity Relation Extraction method for document level Traditional Chinese Medicine texts

Abstract

Chinese medicine is a unique and complex medical system with complete and rich scientific theories. The textual data of Traditional Chinese Medicine (TCM) contains a large amount of relevant knowledge in the field of TCM, which can serve as guidance for accurate disease diagnosis as well as efficient disease prevention and treatment. Existing TCM texts are disorganized and lack a uniform standard. For this reason, this paper proposes a joint extraction framework by using graph convolutional networks to extract joint entity relations on document-level TCM texts to achieve TCM entity relation mining. More specifically, we first finetune the pre-trained language model by using the TCM domain knowledge to obtain the task-specific model. Taking the integrity of TCM into account, we extract the complete entities as well as the relations corresponding to diagnosis and treatment from the document-level medical cases by using multiple features such as word fusion coding, TCM lexicon information, and multi-relational graph convolutional networks. The experimental results show that the proposed method outperforms the state-of-the-art methods. It has an F1-score of 90.7% for Name Entity Recognization and 76.14% for Relation Extraction on the TCM dataset, which significantly improves the ability to extract entity relations from TCM texts. Code is available at https://github.com/xxxxwx/TCMERE.