Customs Commodity HS Code Classification Integrating Text Sequence and Graph Information

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 comprehensively considerthe 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 defin