Named Entity Recognition of Enterprise Annual Report Integrated with BERT

doi:10.16183/j.cnki.jsjtu.2020.009

Abstract

Abstract:

Automatically extracting key data from annual reports is an important means of business assessments. Aimed at the characteristics of complex entities, strong contextual semantics, and small scale of key entities in the field of corporate annual reports, a BERT-BiGRU-Attention-CRF model was proposed to automatically identify and extract entities in the annual reports of enterprises. Based on the BiGRU-CRF model, the BERT pre-trained language model was used to enhance the generalization ability of the word vector model to capture long-range contextual information. Furthermore, the attention mechanism was used to fully mine the global and local features of the text. The experiment was performed on a self-constructed corporate annual report corpus, and the model was compared with multiple sets of models. The results show that the value of F₁ (harmonic mean of precision and recall) of the BERT-BiGRU-Attention-CRF model is 93.69%. The model has a better performance than other traditional models in annual reports, and is expected to provide an automatic means for enterprise assessments.

Key words: named entity recognition, enterprise annual report, BERT, attention mechanism, BiGRU

CLC Number:

TP391.1

ZHANG Jingyi, HE Guanghui, DAI Zhou, LIU Yadong. Named Entity Recognition of Enterprise Annual Report Integrated with BERT[J]. Journal of Shanghai Jiao Tong University, 2021, 55(2): 117-123.

Figures/Tables 10

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References 15

[1]	ZHENG S C, WANG F, BAO H Y, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]∥Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. (Volume 1: Long Papers). Stroudsburg, PA, USA: ACL, 2017: 1227-1236.
[2]	吴文涛，李培峰，朱巧明．基于混合神经网络的实体和事件联合抽取方法[J]. 中文信息学报，2019, 33(8): 77-83.
	WU Wentao, LI Peifeng, ZHU Qiaoming. Joint extraction of entities and events by a hybrid neural network[J]. Journal of Chinese Information Processing, 2019, 33(8): 77-83.
[3]	HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[OL]. (2015-08-09)[2019-11-20]. .
[4]	CHIU J P C, NICHOLS E. Named entity recognition with bidirectional LSTM-CNNs[J]. Transactions of the Association for Computational Linguistics, 2016, 4(1): 357-370.
[5]	MA X Z, HOVY E. End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. (Volume 1: Long Papers). Stroudsburg, PA, USA: ACL, 2016: 1064-1074.
[6]	CHO K, VAN M B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: ACL, 2014: 1724-1734.
[7]	王洁，张瑞东，吴晨生．基于GRU的命名实体识别方法[J]. 计算机系统应用，2018, 27(9): 18-24.
	WANG Jie, ZHANG Ruidong, WU Chensheng. Named entity recognition method based on GRU[J]. Computer Systems & Applications, 2018, 27(9): 18-24.
[8]	BHARADWAJ A, MORTENSEN D, DYER C, et al. Phonologically aware neural model for named entity recognition in low resource transfer settings[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: ACL, 2016: 1462-1472.
[9]	CAO P F, CHEN Y B, LIU K, et al. Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism[C]∥Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: ACL, 2018: 182-192.
[10]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]∥Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA: NIPS, 2017: 5998-6008.
[11]	DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[OL]. (2019-05-24)[2019-11-25]. .
[12]	WITTEN I H, PAYNTER G W, FRANK E, et al. KEA: Practical automatic keyphrase extraction[C]∥Proceedings of the Fourth ACM Conference on Digital Libraries. New York, USA: ACM, 1999: 254-255.
[13]	KANDOLA E J, HOFMANN T, POGGIO T, et al. A neural probabilistic language model[J]. Studies in Fuzziness and Soft Computing, 2006, 194: 137-186.
[14]	KIROS R, ZHU Y K, SALAKHUTDINOV R R, et al. Skip-thought vectors[C]∥Proceedings of the 29th Conference on Neural Information Processing Systems. Montreal, Canada: NIPS, 2015: 3294-3302.
[15]	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. Stroudsburg, PA, USA: ACL, 2017: 2670-2680.

实体类别	实体示例	实体符号
年份	2005年、2016年	YEAR
企业名称	平安银行股份有限公司	COM
财务术语	净利润、营业收入、主营业务收入	FIN
利润类数值	12 626.23万元	PNUM
收入类数值	138.64亿元	SNUM
利润类数值同上年变化趋势	增长13.27%、扭亏为盈	PTREND
收入类数值同上年变化趋势	下降23.12%、创下新高	STREND

语料字符	标注符号	语料字符	标注符号
实	O	入	I-FIN
现	O	1	B-SNUM
营	B-FIN	7	I-SNUM
业	I-FIN	万	I-SNUM
收	I-FIN	元	I-SNUM

项目	字符个数	实体标记字符个数
训练集	384 192	169 263
开发集	128 064	55 294
测试集	133 006	57 426

实体符号	训练集	开发集	测试集
YEAR	4 884	1 628	1 644
COM	4 182	1 194	1 292
FIN	12 213	4 271	4 297
PNUM	3 421	1 041	1 031
SNUM	3 162	1 016	1 057
PTREND	2 031	629	604
STREND	2 154	627	649

实体符号	P	R	F₁
YEAR	98.66	98.66	98.66
COM	96.59	93.41	94.97
FIN	95.93	93.07	94.48
PNUM	92.97	89.81	91.37
SNUM	95.83	92.00	93.88
PTREND	91.46	79.44	85.03
STREND	93.05	86.73	89.73