基于实体知识的石油炼化领域命名实体识别OA

Named entity recognition task in the petroleum refining domain suffers from the problems of scarcity of labeled data as well as the existing pre-trained language models cannot recognize domain combination and nested entities well.Based on this,a data augmentation method EEKR(External Entity Knowledge Replacement,EEKR)based on external entity knowledge is firstly proposed,which effectively solves the problem of scarcity of labeled data by introducing an external entity knowledge base and completing data augmentation by replacing it with entities in the labeled data at the entity level.After that,a named entity recognition model IIEKNER(Namd Entity Recognition Incorporating Internal Entity Knowledge,IIEKNER)is proposed,which incorporates internal entity knowledge into the pre-training model by obtaining internal entity embeddings in the labeled samples.Thus,nested and combined entities in the petroleum refining domain can be recognized more accurately.The experimental results show that compared to other models,the IIEKNER model based on EEKR data augmentation method has better recognition performance.