TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy

Lin Sun, Kai Zhang, Fule Ji, Zhenhua Yang


Abstract
Contract analysis can significantly ease the work for humans using AI techniques. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). A novel Text-Of-Interest Convolutional Neural Network (TOI-CNN) is proposed 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. We have collected a large Chinese insurance contract dataset and labeled the critical elements of seven categories to test the performance of the proposed method. The results show the promising performance of our method in the ETIP problem.
Anthology ID:
N19-2022
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
174–181
Language:
URL:
https://aclanthology.org/N19-2022
DOI:
10.18653/v1/N19-2022
Bibkey:
Cite (ACL):
Lin Sun, Kai Zhang, Fule Ji, and Zhenhua Yang. 2019. TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers), pages 174–181, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy (Sun et al., NAACL 2019)
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PDF:
https://aclanthology.org/N19-2022.pdf