Construction of a Syntactic Analysis Map for Yi Shui School through Text Mining and Natural Language Processing Research
This work addresses the challenge of analyzing unstructured TCM texts for researchers in traditional Chinese medicine, though it is incremental as it applies existing NLP methods to a new domain.
This study tackled the problem of extracting structured information from unstructured classical Chinese texts of the Yishui school of Traditional Chinese Medicine by developing a conditional random field model for entity relationship extraction and a neural network-based dependency parser, resulting in tree structure visualizations of grammatical relationships to support knowledge graph construction.
Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis. Most of the information of the Yishui school of traditional Chinese Medicine (TCM) is stored in the form of unstructured classical Chinese text. The key information extraction of TCM texts plays an important role in mining and studying the academic schools of TCM. In order to solve these problems efficiently using artificial intelligence methods, this study constructs a word segmentation and entity relationship extraction model based on conditional random fields under the framework of natural language processing technology to identify and extract the entity relationship of traditional Chinese medicine texts, and uses the common weighting technology of TF-IDF information retrieval and data mining to extract important key entity information in different ancient books. The dependency syntactic parser based on neural network is used to analyze the grammatical relationship between entities in each ancient book article, and it is represented as a tree structure visualization, which lays the foundation for the next construction of the knowledge graph of Yishui school and the use of artificial intelligence methods to carry out the research of TCM academic schools.