Abstract
How to quickly measure the similarity of addresses has become an urgent need in various fields including financial anti-fraud. Traditional string-based similarity calculation methods have not completed this task perfectly. Taking into account the hierarchical nature of addresses, we constructed a framework for calculating the similarity of Chinese addresses. First, the whole address strings are split and annotated with proper level by a LM-LSTM-CRF model, and then sub-string level similarities are calculated. Last, similarity scores are combining by BP neural networks. This framework has achieved good results in practice for financial anti-fraud tasks.
This work is supported by joint project of Beijing Normal University and Credit Harmony Research, and in part by the National Natural Science Foundation of China under grant 71701018. Jing Liu, Jianbin Wang and Changqing Zhang contributed equally to this work.
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Liu, J. et al. (2019). Chinese Address Similarity Calculation Based on Auto Geological Level Tagging. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_42
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