Named entity recognition for process technic based on subordinate structure
Manufacturing enterprises accumulate a large amount of part processing experience mostly in the form of text.How to extract high-quality processing knowledge from the text is a problem yet to be solved.In response to the problem of the subordinate structure entities to be recognized that leads to the ambiguity of entity boundary defi-nition,a multi-network coordinated Chinese named entity recognition method was proposed.In the process of word vector generation by BERT,the characterization ability of word vectors for process entities was improved by domain self-adaptive methods,and at the same time,attention mechanism and hybrid expert network with multi-gate control were introduced in the BiLSTM-CRF model to capture contextual features and entity information.The experiments showed that the proposed method achieved the best performance over other models by achieving the F1 value of 80.15％for the recognition of machined entities of mechanical parts compared with the current mainstream named entity recognition models.
Chinese named entity recognitionmanufacturing processeshybrid expert network with multi-gatedo-main self-adaptive
NETL
NSTL
万方数据
