To address the challenge of recognizing specialized terminology in gas safety, we introduce a novel Named Entity Recognition (NER) model that integrates semantic similarity. Unlike traditional NER algorithms such as BERT+BiLSTM+CRF, this approach incorporates a word similarity weighting layer. This layer improves the recognition of entities related to Chinese urban gas pipeline standards by utilizing a proposed algorithm that maps inter-character similarity to vector weight values within the word embedding layer. Experimental results validate the model's superiority, exhibiting a notable improvement of 0.051 in the F1 metric.
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