A TOI based CNN with Location Regression for Insurance Contract Analysis

Contract analysis with AI techniques can significantly ease the work for humans. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). We present a novel Text-Of-Interest (TOI) convolutional neural network for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. In addition, a location regressor is trained to improve the precision of element localization, called TOI-CNN+LR. In the detection, we devise a novel non-maximum suppression method with the fusion of length and score metrics, called LS-NMS. A large Chinese insurance contract dataset was collected to test the performance of the proposed method. An extensive set of experiments is performed to investigate how TOI-CNN+LR can work effectively in insurance elements tagging and outperforms other state-of-the-art nested NER models.