Computer Science ›› 2021, Vol. 48 ›› Issue (4): 97-103.doi: 10.11896/jsjkx.200900053

• Database & Big Data & Data Science • Previous Articles     Next Articles

Customs Commodity HS Code Classification Integrating Text Sequence and Graph Information

DU Shao-hua1, WAN Huai-yu1, WU Zhi-hao1,2, LIN You-fang1,2   

  1. 1 School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China
    2 Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing 100044,China
  • Received:2020-06-24 Revised:2020-10-04 Online:2021-04-15 Published:2021-04-09
  • About author:DU Shao-hua,born in 1996,postgradua-te.Her main research interests include text mining and so on.(
    WAN Huai-yu,born in 1981,Ph.D,associate professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include social network mining,text mining,user behavior analysis and spatial-temporal data mining.

Abstract: Customs commodity HS code classification is an important international procedure for cross-border trade of enterprises and individuals.HS code classification can be regarded as a text classification problem,that is,given a paragraph of description for a commodity,to determine the category of the commodity represented by HS code.However,this task is more challenging than general text classification task.First,commodity description texts are organized with special hierarchical structures.Then commodity description texts present sequential features at two levels.In addition,the key information in the commodity description text is scattered and the description forms are diverse.Most of the existing classification methods cannot comprehensivelyconsiderthe above factors to capture key information in the commodity description text.In this paper,we proposes a Text Sequence and Graph Information combination Neural Network(TSGINN) to solve the problem of customs commodity HS code classification.The TSGINN defines the HS code classification problem as a subgraph classification problem based on word co-occurrence network,models association between non-contiguous words through graph attention network,and captures multi-level sequential information through hierarchical long short-term memory network.Experiments on the real-world customs datasets show that the classification effect of TSGINN model is better than that of other methods.

Key words: Customs commodity, Graph attention network, HS code, Multi-level sequential information, Text classification

CLC Number: 

  • TP391
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