WCL-BBCD：一种基于对比学习和知识图谱的命名实体识别方法
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
摘要：
命名实体识别任务是信息抽取的核心任务之一。词汇歧义和词汇缩略是命名实体识别率低的重要原因。本文提出了一种新的命名实体识别模型WCL-BBCD（Word对比学习with BERT-BiLSTM-CRF-DBpedia），它融合了对比学习的思想。该模型首先对文本中的句子对进行训练，计算句子对之间的相似度，并根据相似度对用于命名实体识别任务的BERT进行微调，从而缓解单词歧义。然后，微调后的BERT与BiLSTM-CRF相结合，执行命名实体识别任务。最后，结合知识图谱等先验知识对识别结果进行修正，以缓解单词缩写造成的低识别率问题。在CoNLL-2003英文数据集和OntoNotes V5英文数据集上的实验结果表明，该模型的性能优于其他同类模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
