TY - GEN
T1 - A Text Mining based Method for Policy Recommendation
AU - Zhang, Tong
AU - Liu, Mingyi
AU - Ma, Chao
AU - Tu, Zhiying
AU - Wang, Zhongjie
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Governmental administrations frequently release various types of policies to enforce specific rules, laws, or economic stimulus plans. For a specific policy, there are many potential target individuals, companies and organizations. However, it is not always timely for a company to get to know the policies that are suitable to it. It is necessary to develop efficient policy recommendation methods to help companies catch up with useful policies right the first time. In this paper, we propose a policy recommendation method based on text mining. This method consists of three phases: 1) Policy structure division; 2) Attribute extraction; 3) Matching and recommendation. Since most of policy texts contain too many contents that might be suitable for multiple different types of companies, policy text is firstly divided into text fragments, and each text fragment is then divided into multiple elementary discourse units (EDUs) which are used as the basic recommendation unit with optimal granularity. Secondly, by combining a Named Entity Recognition (NER) based extractor with a rule-based extractor and a grammar-based extractor, we extract attribute entities and logical relations between these entities from each EDU. Thirdly, on the basis of the extracted attribute entities and logical relations, we calculate the matching score between each fragmented policy text and each company, and these scores are taken as the sorted criteria of policy recommendation. The policy recommendation results cover policy text fragments, related governmental administrations, attribute entities and logical relations, links to the entire policy text, and so on. On the basis of a data set collected from real world, the effectiveness of the proposed method is validated by the experiments of two different scenarios: 1) there is a list of policy texts, and it is needed to recommended suitable policies to a specific company; 2) there is a list of companies, and it is needed to help them to find suitable policy texts. This work can be considered as an intelligent government affairs service for accurately pushing useful policies to target users, and is of great significance to the modern governance system.
AB - Governmental administrations frequently release various types of policies to enforce specific rules, laws, or economic stimulus plans. For a specific policy, there are many potential target individuals, companies and organizations. However, it is not always timely for a company to get to know the policies that are suitable to it. It is necessary to develop efficient policy recommendation methods to help companies catch up with useful policies right the first time. In this paper, we propose a policy recommendation method based on text mining. This method consists of three phases: 1) Policy structure division; 2) Attribute extraction; 3) Matching and recommendation. Since most of policy texts contain too many contents that might be suitable for multiple different types of companies, policy text is firstly divided into text fragments, and each text fragment is then divided into multiple elementary discourse units (EDUs) which are used as the basic recommendation unit with optimal granularity. Secondly, by combining a Named Entity Recognition (NER) based extractor with a rule-based extractor and a grammar-based extractor, we extract attribute entities and logical relations between these entities from each EDU. Thirdly, on the basis of the extracted attribute entities and logical relations, we calculate the matching score between each fragmented policy text and each company, and these scores are taken as the sorted criteria of policy recommendation. The policy recommendation results cover policy text fragments, related governmental administrations, attribute entities and logical relations, links to the entire policy text, and so on. On the basis of a data set collected from real world, the effectiveness of the proposed method is validated by the experiments of two different scenarios: 1) there is a list of policy texts, and it is needed to recommended suitable policies to a specific company; 2) there is a list of companies, and it is needed to help them to find suitable policy texts. This work can be considered as an intelligent government affairs service for accurately pushing useful policies to target users, and is of great significance to the modern governance system.
KW - Elementary Discourse Unit
KW - Grammar-based Extractor
KW - Intelligent Government Affairs Service
KW - Named Entity Recognition
KW - Policy Recommendation
KW - Text Mining
UR - https://www.scopus.com/pages/publications/85123285473
U2 - 10.1109/SCC53864.2021.00036
DO - 10.1109/SCC53864.2021.00036
M3 - 会议稿件
AN - SCOPUS:85123285473
T3 - Proceedings - 2021 IEEE International Conference on Services Computing, SCC 2021
SP - 233
EP - 240
BT - Proceedings - 2021 IEEE International Conference on Services Computing, SCC 2021
A2 - Carminati, Barbara
A2 - Chang, Carl K.
A2 - Damiani, Ernesto
A2 - Shuiguang, Deng
A2 - Tan, Wei
A2 - Wang, Zhongjie
A2 - Ward, Robert
A2 - Zhang, Jia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Services Computing, SCC 2021
Y2 - 5 September 2021 through 11 September 2021
ER -