Fine-Grained Element Identification in Complaint Text of Internet Fraud
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.
Keywords
Cite
@article{arxiv.2108.08676,
title = {Fine-Grained Element Identification in Complaint Text of Internet Fraud},
author = {Tong Liu and Siyuan Wang and Jingchao Fu and Lei Chen and Zhongyu Wei and Yaqi Liu and Heng Ye and Liaosa Xu and Weiqiang Wan and Xuanjing Huang},
journal= {arXiv preprint arXiv:2108.08676},
year = {2021}
}
Comments
5 pages, 5 figures, 3 tables accepted as a short paper to CIKM 2021