NDLI: Extracting relevant named entities for automated expense reimbursement

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Extracting relevant named entities for automated expense reimbursement

Content Provider	ACM Digital Library
Author	Zhu, Guangyu Bethea, Timothy J. Krishna, Vikas
Abstract	Expense reimbursement is a time-consuming and labor-intensive process across organizations. In this paper, we present a prototype expense reimbursement system that dramatically reduces the elapsed time and costs involved, by eliminating paper from the process life cycle. Our complete solution involves (1) an electronic submission infrastructure that provides multi- channel image capture, secure transport and centralized storage of paper documents; (2) an unconstrained data mining approach to extracting relevant named entities from un-structured document images; (3) automation of auditing procedures that enables automatic expense validation with minimum human interaction. Extracting relevant named entities robustly from document images with unconstrained layouts and diverse formatting is a fundamental technical challenge to image-based data mining, question answering, and other information retrieval tasks. In many applications that require such capability, applying traditional language modeling techniques to the stream of OCR text does not give satisfactory result due to the absence of linguistic context. We present an approach for extracting relevant named entities from document images by combining rich page layout features in the image space with language content in the OCR text using a discriminative conditional random field (CRF) framework. We integrate this named entity extraction engine into our expense reimbursement solution and evaluate the system performance on large collections of real-world receipt images provided by IBM World Wide Reimbursement Center.
Starting Page	1004
Ending Page	1012
Page Count	9
File Format	PDF
ISBN	9781595936097
DOI	10.1145/1281192.1281300
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning Document layout analysis Named entity extraction Conditional random fields
Content Type	Text
Resource Type	Article

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