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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (6): 105-114.doi: 10.6040/j.issn.1672-3961.0.2021.304

• 机器学习与数据挖掘 • 上一篇    下一篇

基于空洞卷积块架构的命名实体识别模型

袁钺1(),王艳丽2,刘勘2,*()   

  1. 1. 北京大学信息管理系，北京 100871
    2. 中南财经政法大学信息与安全工程学院，湖北 武汉 430073
  • 收稿日期:2021-06-09 出版日期:2022-12-20 发布日期:2022-12-23
  • 通讯作者: 刘勘 E-mail:yuangyue@qq.com;liukan@zuel.edu.cn
  • 作者简介:袁钺(1996—)，男，湖北武汉人，博士研究生，主要研究方向为计算型情报分析、自然语言处理、深度学习。E-mail: yuangyue@qq.com
  • 基金资助:
    中央高校基本科研业务费交叉学科创新研究项目(2722021EK016)

Named entity recognition model based on dilated convolutional block architecture

Yue YUAN1(),Yanli WANG2,Kan LIU2,*()   

  1. 1. Department of Information Management, Peking University, Beijing 100871, China
    2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China
  • Received:2021-06-09 Online:2022-12-20 Published:2022-12-23
  • Contact: Kan LIU E-mail:yuangyue@qq.com;liukan@zuel.edu.cn

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摘要：

受到空洞卷积的启发提出面向二维文本嵌入的列式空洞卷积，设计空洞卷积块架构，基于此架构提出命名实体识别模型并开展进一步试验。在命名实体识别试验中，提出的模型的精密度、召回率和F1超越了其他基线模型，分别达到了0.918 7、0.879 4和0.898 6，表明空洞卷积块架构能够获取包含更多上下文信息的文本特征，从而支持模型对上下文长距离依赖特征的捕获和处理。感受野试验表明需要适当调整空洞率以减轻空洞卷积给模型带来的“网格效应”。提出的基于空洞卷积块架构能有效执行命名实体识别任务。

关键词: 命名实体识别, 空洞卷积块架构, 感受野, 神经网络, 深度学习

Abstract:

Inspired by the dilated convolution, a column-wise dilated convolution towards two dimensional text embedding was proposed and a dilated convolutional block architecture was designed. A named entity recognition model based on the architecture was built for further experiments. In the named entity recognition experiment, the model surpassed other baseline models in the metrics of precision, recall, and F1 value, respectively reaching 0.918 7, 0.879 4, and 0.898 6, indicating that the dilated convolutional block architecture obtained features from context information, thereby supporting the extraction of the long-term dependency. The receptive field experiment showed that it was necessary to jointly adjust the dilation rate and the convolution kernel size to reduce the "gridding effect". The dilated convolutional block architecture proposed could effectively perform the task of named entity recognition.

Key words: named entity recognition, dilated convolutional block architecture, receptive field, neural network, deep learning

中图分类号: 

  • TP391

图1

增加卷积核尺寸或空洞率感受野变化示例"

图2

堆叠卷积层时标准卷积和空洞卷积感受野变化"

图3

空洞卷积块结构"

图4

二维文本嵌入与处理图像的空洞卷积"

图5

列式空洞卷积示例"

图6

空洞卷积块架构"

图7

基于空洞卷积块架构的命名实体识别模型"

图8

基于空洞卷积层的命名实体识别模型"

表1

数据集统计"

新闻句子数量 平均句子长度(汉字字符) 最长句子长度(汉字字符) 最短句子长度(汉字字符) 人物标签数量 地点标签数量 组织标签数量
50 729 47 1476 5 19 588 39 394 21 902

表2

命名实体识别试验结果(地点)"

是否有CRF层 模型 P R F1
没有CRF层 Bi-LSTM-Softmax 0.750 2 0.715 0 0.732 2
IDCNN-Softmax 0.769 9 0.770 9 0.770 4
DCL-Bi-LSTM-Softmax 0.792 9 0.758 4 0.775 3
DCBA-Bi-LSTM-Softmax 0.797 6 0.824 8 0.811 0
有CRF层 Bi-LSTM-CRF 0.869 9 0.808 5 0.838 0
IDCNN-CRF 0.900 9 0.824 8 0.861 2
DCL-Bi-LSTM-CRF 0.906 1 0.861 7 0.883 3
DCBA-Bi-LSTM-CRF 0.918 7 0.879 4 0.898 6

表3

命名实体识别试验结果(人物)"

是否有CRF层 模型 P R F1
没有CRF层 Bi-LSTM-Softmax 0.764 5 0.770 7 0.767 6
IDCNN-Softmax 0.797 8 0.783 8 0.790 7
DCL-Bi-LSTM-Softmax 0.816 9 0.816 5 0.816 7
DCBA-Bi-LSTM-Softmax 0.823 0 0.796 9 0.809 7
有CRF层 Bi-LSTM-CRF 0.846 0 0.817 0 0.831 3
IDCNN-CRF 0.849 7 0.815 0 0.832 0
DCL-Bi-LSTM-CRF 0.897 4 0.837 7 0.866 5
DCBA-Bi-LSTM-CRF 0.889 1 0.824 1 0.855 3

表4

命名实体识别试验结果(组织)"

是否有CRF层 模型 P R F1
没有CRF层 Bi-LSTM-Softmax 0.543 3 0.622 1 0.580 0
IDCNN-Softmax 0.631 8 0.676 9 0.653 6
DCL-Bi-LSTM-Softmax 0.633 8 0.652 9 0.643 2
DCBA-Bi-LSTM-Softmax 0.681 1 0.733 3 0.706 2
有CRF层 Bi-LSTM-CRF 0.755 6 0.731 8 0.743 5
IDCNN-CRF 0.774 9 0.770 8 0.772 9
DCL-Bi-LSTM-CRF 0.835 4 0.777 6 0.805 4
DCBA-Bi-LSTM-CRF 0.835 3 0.815 2 0.825 1

表5

命名实体识别试验结果(合计)"

是否有CRF层 模型 P R F1
没有CRF层 Bi-LSTM-Softmax 0.704 4 0.712 9 0.708 6
IDCNN-Softmax 0.747 1 0.754 8 0.751 0
DCL-Bi-LSTM-Softmax 0.765 0 0.754 4 0.759 6
DCBA-Bi-LSTM-Softmax 0.779 0 0.796 2 0.787 5
有CRF层 Bi-LSTM-CRF 0.837 0 0.794 7 0.815 3
IDCNN-CRF 0.855 8 0.810 1 0.832 3
DCL-Bi-LSTM-CRF 0.888 3 0.835 9 0.861 3
DCBA-Bi-LSTM-CRF 0.891 0 0.847 9 0.868 9

图9

使用CRF的模型学习曲线"

图10

使用Softmax的模型学习曲线"

表6

感受野试验设置及结果(设置1)"

模型 r S(高×宽×数量) 感受野 P R F1
DCL-Bi-LSTM-Softmax 1 10×50×1 10 0.642 8 0.646 2 0.644 5
DCBA-Bi-LSTM-Softmax 1 10×50×1 10 0.689 0 0.721 7 0.705 0

表7

感受野试验设置及结果(设置2)"

模型 r S(高×宽×数量) 感受野 P R F1
DCL-Bi-LSTM-Softmax 2 10×50×1 19 0.658 0 0.646 5 0.652 2
DCBA-Bi-LSTM-Softmax 2 10×50×1 19 0.723 4 0.744 2 0.733 6

表8

感受野试验设置及结果(设置3)"

模型 r S(高×宽×数量) 感受野 P R F1
DCL-Bi-LSTM-Softmax 4 10×50×1 37 0.673 2 0.657 5 0.665 3
DCBA-Bi-LSTM-Softmax 4 10×50×1 37 0.754 1 0.756 1 0.755 1

表9

感受野试验设置及结果(设置4)"

模型 r S(高×宽×数量) 感受野 P R F1
DCL-Bi-LSTM-Softmax 8 10×50×1 73 0.650 2 0.643 7 0.647 0
DCBA-Bi-LSTM-Softmax 8 10×50×1 73 0.753 2 0.751 6 0.752 4
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