论文标题
WCL-BBCD：命名实体识别的对比度学习和知识图方法
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
论文作者
论文摘要
命名实体识别任务是信息提取的核心任务之一。单词歧义和缩写是指命名实体低识别率的重要原因。在本文中，我们提出了一种名为“实体识别模型WCL-BBCD”（用Bert-Bilstm-Crf-Dbpedia的单词对比学习）的小说，其中结合了对比学习的概念。该模型首先在文本中训练句子对，计算句子对之间的相似性，以及根据相似性用于指定实体识别任务的微型伯特（Bert），以减轻歧义词。然后，将微调的BERT与BilstM-CRF结合使用，以执行指定的实体识别任务。最后，识别结果与先验知识（例如知识图）相结合，以减轻单词缩写引起的低认识率问题。在CONLL-2003英语数据集和Ontonotes V5英语数据集上进行的实验结果表明，我们的模型在上面胜过其他类似模型。
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.
