PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mining Information Extraction Rules from Datasheets Without Linguistic Parsing
Rakesh Agrawal, Howard Ho, François Jacquenet and Marielle Jacquenet
In: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 22-24 June 2005, Bari, Italy.

Abstract

In the context of the Pangea project at IBM, we needed to design an information extraction module in order to extract some information from datasheets. Contrary to several information extraction systems based on some machine learning techniques that need some linguistic parsing of the documents, we propose an hybrid approach based on association rules mining and decision tree learning that does not require any linguistic processing. The system may be parameterized in various ways that influence the efficiency of the information extraction rules we discovered. The experiments show the system does not need a large training set to perform well.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1313
Deposited By:François Jacquenet
Deposited On:28 November 2005