Fine-Grained Element Identification in Complaint Text of Internet Fraud

• URL: http://arxiv.org/abs/2108.08676v1
• Date: Thu, 19 Aug 2021 13:33:09 GMT
• Title: Fine-Grained Element Identification in Complaint Text of Internet Fraud
• Authors: Tong Liu, Siyuan Wang, Jingchao Fu, Lei Chen, Zhongyu Wei, Yaqi Liu, Heng Ye, Liaosa Xu, Weiqiang Wan, Xuanjing Huang
• Abstract summary: We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various functions, we propose to identify the role of each clause and classify them into different types of fraud element.
• Score: 47.249423877146604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing system dealing with online complaint provides a final decision without explanations. We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various functions, we propose to identify the role of each clause and classify them into different types of fraud element. We construct a large labeled dataset originated from a real finance service platform. We build an element identification model on top of BERT and propose additional two modules to utilize the context of complaint text for better element label classification, namely, global context encoder and label refiner. Experimental results show the effectiveness of our model.

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