Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39578
Title: A generic information extraction architecture for financial applications
Authors: Wee, L.K.A.
Tong, L.C.
Tan, C.L. 
Keywords: Generic information extraction architecture
Message formatting expert
Message intermediate representation
Syntax tree structures
Issue Date: 1999
Source: Wee, L.K.A.,Tong, L.C.,Tan, C.L. (1999). A generic information extraction architecture for financial applications. Expert Systems with Applications 16 (4) : 343-356. ScholarBank@NUS Repository.
Abstract: The advent of computing has exacerbated the problem of overwhelming information. To manage the deluge of information, information extraction systems can be used to automatically extract relevant information from free-form text for update to databases or for report generation. One of the major challenges to the information extraction is the representation of domain knowledge in the task, that is how to represent the meaning of the input text, the knowledge of the field of application, and the knowledge about the target information to be extracted. We have chosen a directed graph structure, a domain ontology and a frame representation, respectively. We have further developed a generic information extraction (GIE) architecture that combines these knowledge structures for the task of processing. The GIE system is able to extract information from free-form text, further infer and derive new information. It analyzes the input text into a graph structure and subsequently unifies the graph and the ontology for extraction of relevant information, driven by the frame structure during a template filling process. The GIE system has been adopted for use in the message formatting expert system, a large-scale information extraction system for a specific financial application within a major bank in Singapore. © 1999 Elsevier Science Ltd. All rights reserved.
Source Title: Expert Systems with Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/39578
ISSN: 09574174
Appears in Collections:Staff Publications

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