  Named Entity Recognition task is one of the core tasks of information
extraction. Word ambiguity and word abbreviation are important reasons for the
low recognition rate of named entities. In this paper, we propose a novel named
entity recognition model WCL-BBCD (Word Contrastive Learning with
BERT-BiLSTM-CRF-DBpedia), which incorporates the idea of contrastive learning.
The model first trains the sentence pairs in the text, calculate similarity
between sentence pairs, and fine-tunes BERT used for the named entity
recognition task according to the similarity, so as to alleviate word
ambiguity. Then, the fine-tuned BERT is combined with BiLSTM-CRF to perform the
named entity recognition task. Finally, the recognition results are corrected
in combination with prior knowledge such as knowledge graphs, so as to
alleviate the low-recognition-rate problem caused by word abbreviations. The
results of experimentals conducted on the CoNLL-2003 English dataset and
OntoNotes V5 English dataset show that our model outperforms other similar
models on.
