WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition

TL;DR

This paper introduces WCL-BBCD, a novel NER model combining contrastive learning, BERT-BiLSTM-CRF, and knowledge graphs to improve recognition accuracy, especially for ambiguous words and abbreviations.

Contribution

The paper proposes a new NER approach integrating contrastive learning with knowledge graphs, enhancing recognition of ambiguous words and abbreviations.

Findings

Outperforms similar models on CoNLL-2003 dataset

Achieves higher accuracy on OntoNotes V5 dataset

Effectively reduces errors caused by word ambiguity and abbreviations

Abstract

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.