Abstract
This paper describes work towards automatically building on-line structured information resources from information sources that are comprised largely of natural language but with some structuring conventions. Such conversion requires two phases: region identification of the incoming documents, and mapping the information they contain into a more structured form. We describe a system that uses decision-tree-based machine learning techniques to build a classifier that can accurately identify document regions and discuss pattern-discovery methods for extracting information from the identified regions. Experiments demonstrate that this approach works well.
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Ma, L., Shepherd, J., Zhang, Y. (2002). Extracting Information from Semistructured Data. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_13
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DOI: https://doi.org/10.1007/3-540-45703-8_13
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