Abstract
This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine hoist fault text. Therefore, this study proposes a named entity recognition method based on domain dictionary embedding. The method first uses the fault domain knowledge of the mine hoist to construct a domain-specialized dictionary and generate a word vector of characteristic words. Secondly, the BERT pre-trained language model is used to obtain dynamic word vectors, and a dictionary adapter is loaded to obtain contextual domain lexical features to improve recognition accuracy. Finally, the conditional random field (CRF) is the model classifier to output the annotation sequence with the highest score. The experimental results show that this model achieves better than several baseline models and effectively improves the accuracy of fault named entity identification for mine hoists. The innovation of this study is the combination of domain dictionary embedding and a BERT pre-trained language model, which improves the accuracy and robustness of named entity recognition. Therefore, the results of this study have essential research significance for improving the accuracy of fault named entity identification of mine hoists and the construction of fault knowledge maps.
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