Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2680-2685.DOI: 10.11772/j.issn.1001-9081.2021071209
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Jie HU1(), Yan HU1, Mengchi LIU2, Yan ZHANG1
Received:
2021-07-12
Revised:
2021-09-18
Accepted:
2021-09-24
Online:
2021-10-08
Published:
2022-09-10
Contact:
Jie HU
About author:
HU Yan, born in 1993, M. S. candidate. Her research interests include natural language processing.Supported by:
通讯作者:
胡婕
作者简介:
胡燕(1993—),女,安徽安庆人,硕士研究生,主要研究方向:自然语言处理;基金资助:
CLC Number:
Jie HU, Yan HU, Mengchi LIU, Yan ZHANG. Chinese named entity recognition based on knowledge base entity enhanced BERT model[J]. Journal of Computer Applications, 2022, 42(9): 2680-2685.
胡婕, 胡燕, 刘梦赤, 张龑. 基于知识库实体增强BERT模型的中文命名实体识别[J]. 《计算机应用》唯一官方网站, 2022, 42(9): 2680-2685.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021071209
数据集 | 领域 | CLUENER 2020 | MSRA | ||
---|---|---|---|---|---|
句子数 | 实体数 | 句子数 | 实体数 | ||
训练集 | — | 5 200 | 10 800 | 16 000 | 32 000 |
评估集 | — | 600 | 1 200 | — | — |
测试集 | GAM | 300 | 500 | — | — |
ENT | 48 | 100 | — | — | |
LOT | 100 | 300 | — | — | |
FIN | 300 | 600 | — | — | |
总计 | 748 | 1 500 | 4 000 | 9 000 |
Tab. 1 Description of datasets
数据集 | 领域 | CLUENER 2020 | MSRA | ||
---|---|---|---|---|---|
句子数 | 实体数 | 句子数 | 实体数 | ||
训练集 | — | 5 200 | 10 800 | 16 000 | 32 000 |
评估集 | — | 600 | 1 200 | — | — |
测试集 | GAM | 300 | 500 | — | — |
ENT | 48 | 100 | — | — | |
LOT | 100 | 300 | — | — | |
FIN | 300 | 600 | — | — | |
总计 | 748 | 1 500 | 4 000 | 9 000 |
参数 | BERT预训练 | NER任务 |
---|---|---|
Epoch | 3 | 30 |
Batchsize | 32 | 30 |
最大句子度 | 180 | 32 |
优化器 | Adam | — |
学习率 | 3E-5 | 5E-5 |
衰减率 | 0.01 | — |
Tab. 2 Parameters of the proposed model
参数 | BERT预训练 | NER任务 |
---|---|---|
Epoch | 3 | 30 |
Batchsize | 32 | 30 |
最大句子度 | 180 | 32 |
优化器 | Adam | — |
学习率 | 3E-5 | 5E-5 |
衰减率 | 0.01 | — |
模型 | CLUENER 2020 | MSRA | ||||
---|---|---|---|---|---|---|
GAM | ENT | LOT | FIN | ALL | ||
文献[ | 70.90 | 87.11 | 82.73 | 77.18 | 76.52 | 87.01 |
OpenKG+ 文献[ | 71.40 | 87.82 | 83.32 | 77.52 | 77.44 | 87.59 |
本文模型 | 71.50 | 88.43 | 84.21 | 78.12 | 78.15 | 88.11 |
Tab. 3 Comparison of F1 scores of models on test sets
模型 | CLUENER 2020 | MSRA | ||||
---|---|---|---|---|---|---|
GAM | ENT | LOT | FIN | ALL | ||
文献[ | 70.90 | 87.11 | 82.73 | 77.18 | 76.52 | 87.01 |
OpenKG+ 文献[ | 71.40 | 87.82 | 83.32 | 77.52 | 77.44 | 87.59 |
本文模型 | 71.50 | 88.43 | 84.21 | 78.12 | 78.15 | 88.11 |
模型 | CLUENER 2020 | MSRA | ||||
---|---|---|---|---|---|---|
准确率 | 召回率 | F1值 | 准确率 | 召回率 | F1值 | |
文献[ | 74.45 | 78.53 | 76.52 | 86.04 | 88.02 | 87.01 |
OpenKG+ 文献[ | 74.52 | 80.60 | 77.44 | 86.23 | 89.01 | 87.59 |
本文模型 | 76.26 | 80.13 | 78.15 | 87.23 | 89.01 | 88.11 |
Tab. 4 Comparison of evaluation indexes of models on test sets
模型 | CLUENER 2020 | MSRA | ||||
---|---|---|---|---|---|---|
准确率 | 召回率 | F1值 | 准确率 | 召回率 | F1值 | |
文献[ | 74.45 | 78.53 | 76.52 | 86.04 | 88.02 | 87.01 |
OpenKG+ 文献[ | 74.52 | 80.60 | 77.44 | 86.23 | 89.01 | 87.59 |
本文模型 | 76.26 | 80.13 | 78.15 | 87.23 | 89.01 | 88.11 |
模型 | CLUENER 2020 | MSRA |
---|---|---|
BERT+BiLSTM | 74.22 | 82.76 |
ERNIE | 75.73 | 83.48 |
BiLSTM+CRF | 71.36 | 80.56 |
本文模型 | 78.15 | 88.11 |
Tab. 5 Comparison of F1 scores of related models
模型 | CLUENER 2020 | MSRA |
---|---|---|
BERT+BiLSTM | 74.22 | 82.76 |
ERNIE | 75.73 | 83.48 |
BiLSTM+CRF | 71.36 | 80.56 |
本文模型 | 78.15 | 88.11 |
1 | RIEDEL S, YAO L M, McCALLUM A, et al. Relation extraction with matrix factorization and universal schemas[C]// Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA: Association for Computational Linguistics, 2013: 74-84. |
2 | CHEN Y B, XU L C, LIU K, et al. Event extraction via dynamic multi-pooling convolutional neural networks[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2015: 167-176. 10.3115/v1/p15-1017 |
3 | DIEFENBACH D, LOPEZ V, SINGH K, et al. Core techniques of question answering systems over knowledge bases: a survey[J]. Knowledge and Information Systems, 2018, 55(3): 529-569. 10.1007/s10115-017-1100-y |
4 | 李源,马磊,邵党国,等. 用于社交媒体的中文命名实体识别[J]. 中文信息学报, 2020, 34(8):61-69. 10.3969/j.issn.1003-0077.2020.08.008 |
LI Y, MA L, SHAO D G, et al. Chinese named entity recognition for social media[J]. Journal of Chinese Information Processing, 2020, 34(8): 61-69. 10.3969/j.issn.1003-0077.2020.08.008 | |
5 | 张毅,王爽胜,何彬,等 .基于BERT 的初等数学文本命名实体识别方法[J].计算机应用, 2022, 42(2): 433-439. 10.11772/j.issn.1001-9081.2021020334 |
ZHANG Y, WANG S S, HE B, et al. Named entity recognition method of elementary mathematical text based on BERT[J]. Journal of Computer Applications, 2022, 42(2): 433-439. 10.11772/j.issn.1001-9081.2021020334 | |
6 | 李韧,李童,杨建喜,等. 基于Transformer-BiLSTM-CRF的桥梁检测领域命名实体识别[J]. 中文信息学报, 2021, 35(4): 83-91. 10.3969/j.issn.1003-0077.2021.04.012 |
LI R, LI T, YANG J X, et al. Bridge inspection named entity recognition based on Transformer-BiLSTM-CRF[J]. Journal of Chinese Information Processing, 2021, 35(4): 83-91. 10.3969/j.issn.1003-0077.2021.04.012 | |
7 | LIU L Y, SHANG J B, REN X, et al. Empower sequence labeling with task-aware neural language model[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 5253-5260. |
8 | LI H B, HAGIWARA M, LI Q, et al. Comparison of the impact of word segmentation on name tagging for Chinese and Japanese[C]// Proceedings of the 9th International Conference on Language Resources and Evaluation. Paris: European Language Resources Association, 2014: 2532-2536. |
9 | ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: Association for Computational Linguistics, 2018: 1554-1564. 10.18653/v1/p18-1144 |
10 | MA R T, PENG M L, ZHANG Q, et al. Simplify the usage of lexicon in Chinese NER[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 5951-5960. 10.18653/v1/2020.acl-main.528 |
11 | LI X N, YAN H, QIU X P, et al. FLAT: Chinese NER using flat-lattice transformer[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2020: 6836-6842. 10.18653/v1/2020.acl-main.611 |
12 | XUE M G, YU B W, LIU T W, et al. Porous lattice transformer encoder for Chinese NER[C]// Proceedings of the 28th International Conference on Computational Linguistics. [S.l.]: International Committee on Computational Linguistics, 2020: 3831-3841. 10.18653/v1/2020.coling-main.340 |
13 | GUI T, MA R T, ZHANG Q, et al. CNN-based Chinese NER with lexicon rethinking[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2019: 4982-4988. 10.24963/ijcai.2019/692 |
14 | GUI T, ZOU Y C, ZHANG Q, et al. A lexicon-based graph neural network for Chinese NER[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2019: 1040-1050. 10.18653/v1/d19-1096 |
15 | 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, Volume 1 (Long and Short Papers). Stroudsburg, PA: Association for Computational Linguistics, 2019:4171-4186. 10.18653/v1/n18-2 |
16 | SUN Y, WANG S H, LI Y K, et al. ERNIE: enhanced representation through knowledge integration[EB/OL]. (2019-04-19) [2021-01-21].. |
17 | JIA C, SHI Y F, YANG Q R, et al. Entity enhanced BERT pre-training for Chinese NER[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2020: 6384-6396. 10.18653/v1/2020.emnlp-main.518 |
18 | XU B, XU Y, LIANG J Q, et al. CN-DBpedia: a never-ending Chinese knowledge extraction system[C]// Proceedings of the 2017 International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, LNCS 10351. Cham: Springer, 2017: 428-438. |
19 | XU L, TONG Y, DONG Q Q, et al. CLUENER2020: fine-grained named entity recognition dataset and benchmark for Chinese[EB/OL]. (2020-01-20) [2021-01-24].. 10.1109/mercon50084.2020.9185296 |
20 | LEVOW G A. The third international Chinese language processing bakeoff: word segmentation and named entity recognition[C]// Proceedings of the 5th SIGHAN Workshop on Chinese Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2006: 108-117. |
21 | BOUMA G. Normalized (pointwise) mutual information in collocation extraction[EB/OL].[2021-01-25].. |
22 | SUN Y, WANG S H, LI Y K, et al. ERNIE 2.0: a continual pre-training framework for language understanding[C]// Proceedings of the 34th Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 8968-8975. 10.1609/aaai.v34i05.6428 |
[1] | Yuqing WANG, Guangli ZHU, Wenjie DUAN, Shuyu LI, Ruotong ZHOU. Sentiment classification model of psychological counseling text based on attention over attention mechanism [J]. Journal of Computer Applications, 2024, 44(8): 2393-2399. |
[2] | Huanliang SUN, Siyi WANG, Junling LIU, Jingke XU. Help-seeking information extraction model for flood event in social media data [J]. Journal of Computer Applications, 2024, 44(8): 2437-2445. |
[3] | Youren YU, Yangsen ZHANG, Yuru JIANG, Gaijuan HUANG. Chinese named entity recognition model incorporating multi-granularity linguistic knowledge and hierarchical information [J]. Journal of Computer Applications, 2024, 44(6): 1706-1712. |
[4] | Hang YU, Yanling ZHOU, Mengxin ZHAI, Han LIU. Text classification based on pre-training model and label fusion [J]. Journal of Computer Applications, 2024, 44(3): 709-714. |
[5] | Yongfeng DONG, Jiaming BAI, Liqin WANG, Xu WANG. Chinese named entity recognition combining prior knowledge and glyph features [J]. Journal of Computer Applications, 2024, 44(3): 702-708. |
[6] | Kaitian WANG, Qing YE, Chunlei CHENG. Classification method for traditional Chinese medicine electronic medical records based on heterogeneous graph representation [J]. Journal of Computer Applications, 2024, 44(2): 411-417. |
[7] | Xiaoyan ZHANG, Zhengyu DUAN. Cross-lingual zero-resource named entity recognition model based on sentence-level generative adversarial network [J]. Journal of Computer Applications, 2023, 43(8): 2406-2411. |
[8] | Jingsheng LEI, Kaijun LA, Shengying YANG, Yi WU. Joint entity and relation extraction based on contextual semantic enhancement [J]. Journal of Computer Applications, 2023, 43(5): 1438-1444. |
[9] | Huiru WANG, Xiuhong LI, Zhe LI, Chunming MA, Zeyu REN, Dan YANG. Survey of multimodal pre-training models [J]. Journal of Computer Applications, 2023, 43(4): 991-1004. |
[10] | Jie HU, Xiaoxi CHEN, Yan ZHANG. Answer selection model based on pooling and feature combination enhanced BERT [J]. Journal of Computer Applications, 2023, 43(2): 365-373. |
[11] | Qingtang LIU, Xinqian MA, Jie ZHOU, Linjing WU, Pengxiao ZHOU. Understanding of math word problems integrating commonsense knowledge base and grammatical features [J]. Journal of Computer Applications, 2023, 43(2): 356-364. |
[12] | Xudong HOU, Fei TENG, Yi ZHANG. Medical named entity recognition model based on deep auto-encoding [J]. Journal of Computer Applications, 2022, 42(9): 2686-2692. |
[13] | Guanyou XU, Weisen FENG. Python named entity recognition model based on transformer [J]. Journal of Computer Applications, 2022, 42(9): 2693-2700. |
[14] | Yayao ZUO, Haoyu CHEN, Zhiran CHEN, Jiawei HONG, Kun CHEN. Named entity recognition method combining multiple semantic features [J]. Journal of Computer Applications, 2022, 42(7): 2001-2008. |
[15] | Yi ZHANG, Shuangsheng WANG, Bin HE, Peiming YE, Keqiang LI. Named entity recognition method of elementary mathematical text based on BERT [J]. Journal of Computer Applications, 2022, 42(2): 433-439. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||