WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
This work addresses Named Entity Recognition for information extraction, offering an incremental improvement by integrating existing techniques to handle specific challenges.
The authors tackled the problem of low recognition rates in Named Entity Recognition caused by word ambiguity and abbreviations by proposing WCL-BBCD, a model combining contrastive learning, BERT-BiLSTM-CRF, and knowledge graphs, which outperformed similar models on CoNLL-2003 and OntoNotes V5 datasets.
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.