Enterprise portrait construction method based on label layering and deepening modeling

doi:10.11772/j.issn.1001-9081.2021071248

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1170-1177.DOI: 10.11772/j.issn.1001-9081.2021071248

Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles

Enterprise portrait construction method based on label layering and deepening modeling

Xingshuo DING¹, Xiang LI¹(), Qian XIE²^,³

^1.Faculty of Computer and Software，Huaiyin Institute of Technology，Huaian Jiangsu 223003，China
^2.Jiangsu Eazytec Company Limited，Yixing Jiangsu 214200，China
^3.Nanjing Byosoft Company Limited，Nanjing Jiangsu 210032，China

Received:2021-07-16 Revised:2021-09-01 Accepted:2021-09-14 Online:2021-09-18 Published:2022-04-10
Contact: Xiang LI
About author:DING Xingshuo， born in 1996， M. S. candidate. His research interests include data mining， enterprise portrait.
XIE Qian， born in 1976， M. S.， professor. His research interests include cloud computing， big data.
Supported by:
National Natural Science Foundation of China(71874067);Jiangsu Industry University Research Cooperation Project(BY2020067);Huaiyin Institute of Technology Graduate Science and Technology Innovation Program(HGYK202120)

基于标签分层延深建模的企业画像构建方法

丁行硕¹, 李翔¹(), 谢乾²^,³

^1.淮阴工学院计算机与软件工程学院, 江苏淮安 223003
^2.江苏卓易信息科技股份有限公司, 江苏宜兴 214200
^3.南京百敖软件有限公司, 南京 210032

通讯作者: 李翔
作者简介:丁行硕（1996—），男，山东枣庄人，硕士研究生，主要研究方向：数据挖掘、企业画像
谢乾（1976—），男，江苏无锡人，教授，硕士，主要研究方向：云计算、大数据。
基金资助:
国家自然科学基金资助项目(71874067);江苏省产学研合作项目(BY2020067);淮阴工学院研究生科技创新计划项目(HGYK202120)

Abstract

Abstract:

Label modeling is the basic task of label system construction and portrait construction. Traditional label modeling methods have problems such as difficulty in processing fuzzy labels， unreasonable label extraction， and ineffective integration of multi-modal entities and multi-dimensional relationships. Aiming at these problems， an enterprise profile construction method based on label layering and deepening modeling， called EPLLD （Enterprise Portrait of Label Layering and Deepening）， was proposed. Firstly， the multi-characteristic information was extracted through multi-source information fusion， and the fuzzy labels of enterprises （such as labels in wholesale and retail industries that cannot fully summarize the characteristics of enterprises） were counted and screened. Secondly， the professional domain lexicon was established for feature expansion， and the BERT （Bidirectional Encoder Representation from Transformers） language model was combined for multi-feature extraction. Thirdly， Bi-directional Long Short-Term Memory （BiLSTM） was used to obtain fuzzy label deepening results. Finally， the keywords were extracted through TF-IDF （Term Frequency-Inverse Document Frequency）， TextRank， and Latent Dirichlet Allocation （LDA） model to achieve label layering and deepening modeling. Experimental analysis on the same enterprise dataset shows that the precision of EPLLD in the fuzzy label deepening task is 91.11%， which is higher than those of 8 label processing methods such as BiLSTM+Attention and BERT+Deep CNN.

Key words: enterprise portrait, label modeling, multi-source information fusion, fuzzy label, feature extraction

摘要：

标签建模是标签体系建设和画像构建的基本任务。而传统标签建模方法存在模糊标签处理难、标签提取不合理，以及无法有效融合多模实体和多维关系等问题。针对以上问题提出了一种基于标签分层延深建模的企业画像构建方法EPLLD。首先，通过多源信息融合获取多特征信息，并对企业模糊标签（如批发、零售等行业中的不能完整概括企业特点的标签）进行统计和筛选；然后，建立专业领域词库进行特征拓展，并结合BERT语言模型进行多特征提取；其次，利用双向长短期记忆（BiLSTM）网络获取模糊标签延深结果；最后，通过TF-IDF、TextRank、隐含狄利克雷分布（LDA）模型提取关键词，从而实现标签的分层延深建模。在同一企业数据集上进行实验分析，结果表明在模糊标签延深任务中EPLLD的精确率达到91.11%，高于BiLSTM+Attention、BERT+Deep CNN等8种标签处理方法。

关键词: 企业画像, 标签建模, 多源信息融合, 模糊标签, 特征提取

CLC Number:

TP391.1

Xingshuo DING, Xiang LI, Qian XIE. Enterprise portrait construction method based on label layering and deepening modeling[J]. Journal of Computer Applications, 2022, 42(4): 1170-1177.

丁行硕, 李翔, 谢乾. 基于标签分层延深建模的企业画像构建方法[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1170-1177.

Figures/Tables 13

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标签类型	一级指标	二级指标	标签描述	模糊标记
统计类标签	基本信息	公司简介、地理信息、工商信息	当前企业注册信息	行业类型出现贸易、批发业、零售业等
	科研信息	软著信息、专利信息、著作权	当前申请的科研信息	所属领域、类型、研究热点等
	其他信息	企业荣誉、资质证书	当前获得的荣誉和资质	不易出现
规则类标签	企业规模	员工数量、企业资金	当前员工数量和资金情况	不易出现
	企业价值	x₁，x₂，…，x_n	制定评价指标综合评价
	风险等级	处罚信息	统计处罚信息
挖掘类标签	企业需求	招聘信息	挖掘当前招聘信息	企业特点、需求不明确等
挖掘类标签	企业特点	y₁，y₂，…，y_n	使用TF-IDF关键词提取	企业特点、需求不明确等

标签类型	一级指标	二级指标	标签描述	模糊标记
统计类标签	基本信息	公司简介、地理信息、工商信息	当前企业注册信息	行业类型出现贸易、批发业、零售业等
	科研信息	软著信息、专利信息、著作权	当前申请的科研信息	所属领域、类型、研究热点等
	其他信息	企业荣誉、资质证书	当前获得的荣誉和资质	不易出现
规则类标签	企业规模	员工数量、企业资金	当前员工数量和资金情况	不易出现
	企业价值	x₁，x₂，…，x_n	制定评价指标综合评价
	风险等级	处罚信息	统计处罚信息
挖掘类标签	企业需求	招聘信息	挖掘当前招聘信息	企业特点、需求不明确等
挖掘类标签	企业特点	y₁，y₂，…，y_n	使用TF-IDF关键词提取	企业特点、需求不明确等

数据集	企业文档篇数	句子数
训练集	146 934	151 488
验证集	48 978	50 485
测试集	48 978	50 606

数据集	企业文档篇数	句子数
训练集	146 934	151 488
验证集	48 978	50 485
测试集	48 978	50 606

模型	精确率	召回率	F1_score
KNN	79.70	80.23	79.83
NBC	81.30	82.05	81.43
CNN	88.31	88.47	88.38
Deep CNN	89.18	89.37	89.26
BiGRU	89.41	89.59	89.48
BiLSTM+Attention	89.45	89.45	89.37
RNN+CNN	89.68	89.69	89.68
BERT+Deep CNN	90.47	90.47	90.45
EPLLD	91.11	91.12	91.08

Enterprise portrait construction method based on label layering and deepening modeling

基于标签分层延深建模的企业画像构建方法

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Figures/Tables 13

References 34

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建模方法	一级标签	二级标签	三级标签	分词标签
EPLLD模型	批发	商贸	化工、钢材、工程	橡胶制品、建筑、施工等
传统分析处理模型	化工、工程			橡胶制品、建筑、施工等

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