Research On The Method Of Relation Extraction In Biomedical Domain

With the rapid development of the biomedical field,the amount of biomedical literature has exploded,containing rich biomedical information.Biomedical relation extraction is a natural language processing technology which aims to extract the relation between entities from biomedical text data.This technology can help researchers quickly extract important biomedical information from the literature,providing important support for drug development and disease treatment.Compared to the general field,biomedical texts are mostly long and difficult sentences,and entities are mostly domain terms distributed in different clauses.Meanwhile,there is semantic similarity in biomedical texts,resulting in less significant differences between types.This thesis conducts the following research on the problems and challenges in biomedical relation extraction tasks.Aiming at the problem that biomedical entities are mostly domain terms,and the model is difficult to fully understand their semantics,we propose a drug-drug interaction relation extraction model based on domain knowledge and graph neural network.In biomedical texts,entities are usually distributed in different sentences,which leads to insufficient connections between entities,and the model cannot fully learn the information of entities.The model proposed can use domain knowledge to enhance entity representation,and use syntactic information to learn sentence sequence information and long-distance grammatical relations.Experimental results show that the performance of the model in drug-drug interaction relation extraction can be improved by fusing domain knowledge and syntactic information.Aiming at the problem that in biomedical datasets,many texts with similar semantic relation types lead to low discrimination between different types,we propose a biomedical relation extraction model based on syntactic enhanced contrastive networks.By introducing contrastive learning to change the position of different types in the embedding space,the model can gather clusters of the same type of points together,making different types of point clusters more dispersed.The experimental results show that the model can effectively enhance the similarity between the same types and the differences between different types to improve the performance of biomedical relation extraction.Aiming at the problem that there are differences in the number of samples of each type in the biomedical dataset,resulting in low classification accuracy for types with fewer training samples,we propose a biomedical relations extraction model based on prompt learning.Guide the model to focus on key features and information through prompt templates and use the prior knowledge of pre-trained language models to enhance its predictive and generalization capabilities.The experimental results show that introducing prompt learning can obtain more knowledge from limited data,effectively alleviating the problem of insufficient knowledge that the model can learn when the data volume is small,thereby improving the performance of the model in biomedical relation extraction.