Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (6): 105-114.doi: 10.6040/j.issn.1672-3961.0.2021.304

• Machine Learning & Data Mining • Previous Articles     Next Articles

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

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

CLC Number: 

  • TP391

Fig.1

Examples of receptive field expansion when increasing kernel size S or dilation rate r"

Fig.2

Comparison of receptive field between stacking standard convolutional layers or dilated convolutional layers"

Fig.3

Structure of dilated convolutional block"

Fig.4

Two-dimensional text embedding and dilated convolution on picture"

Fig.5

Column-wise dilated convolution"

Fig.6

Dilated convolutional block architecture"

Fig.7

Named entity recognition model based on dilated convolutional block architecture"

Fig.8

Named entity recognition model based on dilated convolutional layer"

Table 1

Statistics of dataset  个"

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

Table 2

Experiment results of named entity recognition (Location)"

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

Table 3

Experiment results of named entity recognition (Person)"

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

Table 4

Experiment results of named entity recognition (Organization)"

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

Table 5

Experiment results of named entity recognition (Total)"

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

Fig.9

Learning curves for models using CRF"

Fig.10

Learning curves for models using Softmax"

Table 6

Settings and results for receptive field experiments (Setting 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

Table 7

Settings and results for receptive field experiments (Setting 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

Table 8

Settings and results for receptive field experiments (Setting 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

Table 9

Settings and results for receptive field experiments (Setting 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
1 PANCHENDRARAJAN R, AMARESAN A. Bidirectional LSTM-CRF for named entity recognition[C]//Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation. Hong Kong, China: Association for Computational Linguistics, 2018: 531-540.
2 LI L , XU W , YU H . Character-level neural network model based on Nadam optimization and its application in clinical concept extraction[J]. Neurocomputing, 2020, 414 (16): 182- 190.
3 SHARMA R , MORWAL S , AGARWAL B , et al. A deep neural network-based model for named entity recognition for Hindi language[J]. Neural Computing and Applications, 2020, 32 (20): 16191- 16203.
doi: 10.1007/s00521-020-04881-z
4 WU C , WU F , QI T , et al. Detecting entities of works for chinese chatbot[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2020, 19 (6): 1- 13.
5 LI X , ZHANG H , ZHOU X H . Chinese clinical named entity recognition with variant neural structures based on BERT methods[J]. Journal of Biomedical Informatics, 2020, 107 (18): 103422.
6 JIA C, SHI Y, YANG Q, et al. Entity enhanced bert pre-training for chinese NER[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Punta Cana, Dominica: Association for Computational Linguistics, 2020: 6384-6396.
7 HAN Y, YAN Y, HAN Y, et al. Chinese grammatical error diagnosis based on RoBERTa-BiLSTM-CRF model[C]//Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications. Suzhou, China: Association for Computational Lingu-istics, 2020: 97-101.
8 YANG Z, DAI Z, YANG Y, et al. Xlnet: generalized autoregressive pretraining for language understanding[C]// Proceedings of Advances in Neural Information Processing Systems. Vancouver, Canada: MIT Press, 2019: 5753-5763.
9 DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, USA: Association for Computational Linguistics, 2019: 4171-4186.
10 ZHANG Z, HAN X, LIU Z, et al. ERNIE: enhanced language representation with informative entities[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, 2019: 1441-1451.
11 CHEN L C , PAPANDREOU G , KOKKINOS I , et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40 (4): 834- 848.
12 WANG Z , JI S . Smoothed dilated convolutions for improved dense prediction[J]. Data Mining and Knowledge Discovery, 2021, 35 (4): 1- 27.
13 MEHTA S, RASTEGARI M, CASPI A, et al. Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich, Germany: Springer, 2018: 552-568.
14 STRUBELL E, VERGA P, BELANGER D, et al. Fast and accurate entity recognition with iterated dilated convolutions[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics, 2017: 2670-2680.
15 KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P. A convolutional neural network for modelling sentences[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore, USA: Association for Computational Linguistics, 2014: 655-665.
16 GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Ft. Lauderdale, USA: AISTATS, 2011: 315-323.
17 HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 770-778.
18 WANG P, CHEN P, YUAN Y, et al. Understanding convolution for semantic segmentation[C]//Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Tahoe City, USA: IEEE, 2018: 1451-1460.
19 LIPPMANN R, CAMPBELL W, CAMPBELL J. An overview of the darpa data driven discovery of models (d3m) program[C]//Proceedings of 29th Conference on Neural Information Processing Systems. Barcelona, Spain: MIT Press, 2016: 1-2.
[1] Tongyu JIANG,Fan CHEN,Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8, 18.
[2] Jianqing WU,Xiuguang SONG. Review on development of simultaneous localization and mapping technology [J]. Journal of Shandong University(Engineering Science), 2021, 51(5): 16-31.
[3] DING Fei, JIANG Mingyan. Housing price prediction based on improved lion swarm algorithm and BP neural network model [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 8-16.
[4] YIN Xiaomin, MENG Xiangjian, HOU Kunming, CHEN Yaxiao, GAO Feng. Correction method for historical output data of photovoltaic power plant considering spatial correlation based on artificial neural network [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 118-123.
[5] YANG Xiuyuan, PENG Tao, YANG Liang, LIN Hongfei. Adaptive multi-domain sentiment analysis based on knowledge distillation [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 15-21.
[6] Qingfa CHAI,Shoujing SUN,Jifu QIU,Ming CHEN,Zhen WEI,Wei CONG. Prediction method of power grid emergency supplies under meteorological disasters [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 76-83.
[7] LIAO Jinping, MO Yuchang, YAN Ke. Model and application of short-term electricity consumption forecast based on C-LSTM [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 90-97.
[8] HUO Bingqiang, ZHOU Tao, LU Huiling, DONG Yali, LIU Shan. Lung tumor benign-malignant classification based on multi-modal residual neural network and NRC algorithm [J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 59-67.
[9] Zhiwei WANG,Nan GE,Chunwei LI. Fuzzy control of structure vibration mode based on BP neural network algorithm [J]. Journal of Shandong University(Engineering Science), 2020, 50(5): 13-19.
[10] SUN Donglei, WANG Yan, YU Yixiao, HAN Xueshan, YANG Ming, YAN Fangqing. Interval prediction of short-term regional photovoltaic power based on BP neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(5): 70-76.
[11] Guoyong CAI,Xinhao HE,Yangyang CHU. Visual sentiment analysis based on spatial attention mechanism and convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 8-13.
[12] LIAO Nanxing, ZHOU Shibin, ZHANG Guopeng, CHENG Deqiang. Image caption generation method based on class activation mapping and attention mechanism [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 28-34.
[13] LIU Shuai, WANG Lei, DING Xutao. Emotional EEG recognition based on Bi-LSTM [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 35-39.
[14] Yifei LI,Zunhua GUO. A Chirplet neural network for automatic target recognition [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 8-14.
[15] Baoming JIN,Guangyi LU,Wei WANG,Lunyue DU. Research on BP neural network rainfall runoff forecasting model based on elastic gradient descent algorithm [J]. Journal of Shandong University(Engineering Science), 2020, 50(3): 117-124.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] HE Dongzhi, ZHANG Jifeng, ZHAO Pengfei. Parallel implementing probabilistic spreading algorithm using MapReduce programming mode[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 0, (): 22 -28 .
[2] LIU Yun,QIU Xiao-guo . COD determination by interpolation of the  TOC coefficient method[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(4): 108 -117 .
[3] DUN Yue-Qin, MIN Yue, YUAN Jian-Sheng. Characteristic analysis of the forward response of array lateral logging[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(1): 121 -125 .
[4] WU Jun-fei ,WANG Wei-qiang ,HU De-dong ,CUI Yu-liang , . he explosive energy analysis and calculation of pingyin urea reactor body[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 80 -83 .
[5] TIAN Wei,QIAO Yi-zheng,MA Zhi-qiang . Offline Chinese signature verification based on the second feature extraction by DWT[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(3): 55 -59 .
[6] LIN Yan,WEI Dong, . LIN Yan1,WEI Dong2[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(3): 103 -107 .
[7] ZHENG Gui-lan,MA Qing-lei,PAN Xiu-hua . Research on a design method of asphalt overlay on the used cement concrete pavement[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(4): 93 -97 .
[8]

YANG Guohui1, SUN Xiaoyu1,2*, TSUBAKI Noritatsu1

. Zeolite capsule catalyst for biogasoline[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 92 -97 .
[9] ZHAO Wei, AI Hongqi. pH effect on the structure of Aβ42 fibrils[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 134 -138 .
[10] ZHONG Qian-qian, YUE Qin-yan*, LI Qian, LI Ying, XU Xing, GAO Bao-yu. Kinetics of the adsorption of Reactive Brilliant Red K-2BP onto    modified wheat residue[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(1): 133 -139 .