[Purpose/Significance] The technique of word separation and named entity recognition in the field of ancient Chinese medical texts is investigated without annotated resources, based on the word separation and named entity recognition model, the word separation is carried out and the language model is trained for the text in the field of Chinese medicine. [Method/Process] In the training process, the study used a multi-task learning framework of entity concept ranking prediction and masked word prediction to effectively incorporate prior conceptual knowledge from the lexicon into the language model, and obtain a language model that integrates the semantics of the discourse with prior knowledge. Starting from the MLM task used in the model training, a text generation task based on the completion type was designed to perform knowledge citation of a single ancient text, traversing all the phrases in the single text and performing full citation of knowledge concepts based on the phrase-entity path, and discovering the implicit knowledge structure from the single text based on the mining of a priori rules to construct the implicit chapter structure. [Result/Conclusion] The comparative experiments show that the proposed text citation approach can effectively utilize the model’s prior knowledge in only five annotated samples, which can well solve the problem of knowledge citation of ancient Chinese medical texts in the absence of annotation compared with the traditional method, and provide a solution for further realization of the parsing of single texts of ancient Chinese medical texts. It is of great theoretical and practical significance for the development of traditional Chinese medicine and modern medicine, as well as the research of medical history to sort out, proofread and annotate ancient Chinese medicine books and dig out the knowledge contained in them.
Liu Yao
,
Li Guanlin
,
Li Huanqing
. Knowledge Indexing and Structural Analysis Techniques for Single Text of Ancient Chinese Medical Books[J]. Library and Information Service, 2022
, 66(24)
: 118
-127
.
DOI: 10.13266/j.issn.0252-3116.2022.24.011
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