端到端的嵌套命名实体识别方法研究

doi:10.3778/j.issn.1002-8331.2109-0498

摘要/Abstract

摘要： 命名实体识别（NER）被视为自然语言处理中的一项基础性研究任务。受计算机视觉中单阶段（one-stage）目标检测算法启发，借鉴其算法思想并引入回归运算，提出有效识别嵌套命名实体的端到端方法。基于多目标学习框架，利用深度神经网络将句子转换为文本特征图以回归预测嵌套实体边界，设计中心度方法抑制低质量边界。与多种方法在ACE2005中文数据集上进行对比实验。实验结果表明，该方法有效识别文本中的嵌套命名实体，且计算机视觉算法思想和边界回归机制在自然语言处理任务中取得理想的效果。

关键词: 嵌套命名实体, 回归运算, 中心度, 端到端, 多目标学习

Abstract: Named entity recognition（NER） is regarded as a basic research in natural language processing. Inspired by the one-stage object detection algorithm in computer vision, this paper proposes an effective end-to-end method for identifying nested named entities by using its algorithm idea and introducing regression operation. Based on multi-task learning framework, the paper uses deep neural network to transform sentences into text feature graphs to regress nested entity boundaries, and designs centrality method to suppress low-quality boundaries. A comparative experiment is carried out with several methods on ACE2005 Chinese dataset. Experiments show that this method is effective in identifying nested named entities in text, and the computer vision algorithm idea and boundary regression mechanism achieve ideal results in natural language processing tasks.

Key words: nest named entities, regression operation, center score, end to end, multi-tasklearning

邓力源, 陈艳平, 武乐飞, 秦永彬, 黄瑞章, 郑庆华, 谭曦. 端到端的嵌套命名实体识别方法研究[J]. 计算机工程与应用, 2023, 59(7): 278-284.

DENG Liyuan, CHEN Yanpin, WU Yuefei, QIN Yongbin, HUANG Ruizhang, ZHENG Qinghua, TAN Xi. Research on End-To-End Nested Named Entity Recognition Method[J]. Computer Engineering and Applications, 2023, 59(7): 278-284.

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