Harvest - a System for Creating Structured Rate Filing Data from Filing PDFs

被引：0

作者：

Tekin, Ender ^{[1
]}

You, Qian ^{[2
]}

Conathan, Devin M. ^{[1
]}

Fung, Glenn M. ^{[1
]}

Kneubuehl, Thomas S. ^{[1
]}

机构：

[1] Amer Family Mutual Insurance Co SI, Madison, WI 53783 USA

[2] Coupang Corp, Seoul, South Korea

来源：

THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2022年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. This process, which we call Harvest, significantly reduces the time needed to extract tabular information from PDF files, enabling analysis of such data at a speed and scale that was previously unattainable.

引用

页码：12414 / 12422

页数：9

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