CareerMiner: Automatic extraction of professional network from large Chinese resume data
The professional contacts of a person, including their past colleagues and supervisors, can play an important role in job recommendation and intelligent human resources management. However, collecting such information on a large scale can be challenging and costly. In this paper, we propose CareerMi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Franklin Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773186323000592 |
| _version_ | 1797202939359526912 |
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| author | Qiqi Chen Dexu Kong Yongchi Zhu Zitao Shen Chengyue Lu Yang Li Lin Zhang |
| author_facet | Qiqi Chen Dexu Kong Yongchi Zhu Zitao Shen Chengyue Lu Yang Li Lin Zhang |
| author_sort | Qiqi Chen |
| collection | DOAJ |
| description | The professional contacts of a person, including their past colleagues and supervisors, can play an important role in job recommendation and intelligent human resources management. However, collecting such information on a large scale can be challenging and costly. In this paper, we propose CareerMiner, a system to automatically construct a dynamic professional network to capture people’s colleague and supervisor relationships based on inter-related work experiences in a large collection of Chinese resumes. Specifically, we extract fine-grained work affiliation and job title information from resume text using a customized Name Entity Recognition model and introduce a trie-like data structure to efficiently index the hierarchical affinities of the implicit organizational structure in work experiences. Then, the topology of leaf nodes is used to identify professional relationships. Experimental results on real-world resume data are presented to demonstrate the effectiveness of the proposed system. Additionally, we develop an open-source demo system that includes the main features of the system. We also conduct a case study to illustrate how CareerMiner can be utilized for the social science research and data mining applications of professional networks. |
| first_indexed | 2024-03-08T16:50:53Z |
| format | Article |
| id | doaj.art-3d6a97662c014dd38787592f3b7d7ad1 |
| institution | Directory Open Access Journal |
| issn | 2773-1863 |
| language | English |
| last_indexed | 2024-04-24T08:11:24Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj.art-3d6a97662c014dd38787592f3b7d7ad12024-04-17T04:50:29ZengElsevierFranklin Open2773-18632024-03-016100065CareerMiner: Automatic extraction of professional network from large Chinese resume dataQiqi Chen0Dexu Kong1Yongchi Zhu2Zitao Shen3Chengyue Lu4Yang Li5Lin Zhang6Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, ChinaTsinghua University, Beijing, China; Corresponding author.Tsinghua University, Beijing, ChinaThe professional contacts of a person, including their past colleagues and supervisors, can play an important role in job recommendation and intelligent human resources management. However, collecting such information on a large scale can be challenging and costly. In this paper, we propose CareerMiner, a system to automatically construct a dynamic professional network to capture people’s colleague and supervisor relationships based on inter-related work experiences in a large collection of Chinese resumes. Specifically, we extract fine-grained work affiliation and job title information from resume text using a customized Name Entity Recognition model and introduce a trie-like data structure to efficiently index the hierarchical affinities of the implicit organizational structure in work experiences. Then, the topology of leaf nodes is used to identify professional relationships. Experimental results on real-world resume data are presented to demonstrate the effectiveness of the proposed system. Additionally, we develop an open-source demo system that includes the main features of the system. We also conduct a case study to illustrate how CareerMiner can be utilized for the social science research and data mining applications of professional networks.http://www.sciencedirect.com/science/article/pii/S2773186323000592Professional networkChinese resume analysisSocial network extractionSocial relationship miningTrie |
| spellingShingle | Qiqi Chen Dexu Kong Yongchi Zhu Zitao Shen Chengyue Lu Yang Li Lin Zhang CareerMiner: Automatic extraction of professional network from large Chinese resume data Franklin Open Professional network Chinese resume analysis Social network extraction Social relationship mining Trie |
| title | CareerMiner: Automatic extraction of professional network from large Chinese resume data |
| title_full | CareerMiner: Automatic extraction of professional network from large Chinese resume data |
| title_fullStr | CareerMiner: Automatic extraction of professional network from large Chinese resume data |
| title_full_unstemmed | CareerMiner: Automatic extraction of professional network from large Chinese resume data |
| title_short | CareerMiner: Automatic extraction of professional network from large Chinese resume data |
| title_sort | careerminer automatic extraction of professional network from large chinese resume data |
| topic | Professional network Chinese resume analysis Social network extraction Social relationship mining Trie |
| url | http://www.sciencedirect.com/science/article/pii/S2773186323000592 |
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