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Extracting Relevant Named Entities for Automated Expense Reimbursement

Extracting Relevant Named Entities for Automated Expense Reimbursement

This video was recorded at 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007. Expense reimbursement is a time-consuming and labor-intensive process across organizations. In this talk, we present an automated expense reimbursement system developed at IBM Almaden Research Center. Our complete solution involves (1) an electronic document management infrastructure that provides multi-channel image capture, transport and storage of paper documents, such as receipts; (2) an unconstrained data mining approach to extracting relevant named entities from un-structured document images; (3) automation of manual auditing procedures using extracted metadata. The main focus of this presentation is our approach to automatically extracting important metadata, once we aggregate documents through such a scalable infrastructure. 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 contexts, such as language constructs and punctuation. We present a novel approach for extracting relevant named entities from document images by learning the statistical dependencies between page layout and language features collectively from the sequence of geometrically decomposed regions on a document using a discriminative conditional random fields (CRFs) 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.


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