Abstract
Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity recognition. Most previous sequential labeling models are task-specific, while recent years have witnessed the rising of generative models due to the advantage of unifying all NER tasks into the seq2seq model framework. Although achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task. More concretely, we achieve entity boundary detection by classifying the relations between tokens within the sentence. To improve the accuracy of entity-type mapping during decoding, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information into the model via the self and cross-attention mechanisms. We perform experiments on an extensive set of NER benchmarks, including two flat, three nested, and three discontinuous NER datasets. Experimental results show that our approach considerably improves the generative NER model's performance.
Abstract (translated)
命名实体识别(NER)是自然语言处理中一个重要的研究问题。NER任务有三种类型,包括平面实体识别、嵌套实体识别和离散实体识别。过去,大多数顺序标记模型都是任务特定的,而近年来由于将所有NER任务统一到序列到序列模型框架的优势,生成模型的数量不断增加。尽管取得了良好的性能,但我们的初步研究表明,现有的生成模型在实体边界估计和实体类型估计方面无效。本文提出了多任务Transformer模型,将实体边界检测任务融入到命名实体识别任务中。更具体地说,我们通过分类句子中 tokens之间的关系来实现实体边界检测。为了改善解码时实体类型映射的准确性,我们采用了外部知识库来计算先前实体类型分布,然后通过自我和交叉注意力机制将信息注入到模型中。我们进行了广泛的NER基准测试集实验,包括两个平面、三个嵌套和三个离散实体识别数据集。实验结果显示,我们的方法显著提高了生成NER模型的性能。
URL
https://arxiv.org/abs/2303.10870
