Abstract
We present a hybrid machine learning approach for information extraction from unstructured documents by integrating a learned classifier based on the Maximum Entropy Modeling (MEM), and a classifier based on our work on Data-Oriented Parsing (DOP). The hybrid behavior is achieved through a voting mechanism applied by an iterative tag-insertion algorithm. We have tested the method on a corpus of German newspaper articles about company turnover, and achieved 85.2% F-measure using the hybrid approach, compared to 79.3% for MEM and 51.9% for DOP when running them in isolation.
Thanks to Volker Morbach for his great help during the implementation and evaluation phase of the project. This work was supported by a research grant from BMBF to the DFKI project Quetal (FKZ: 01 IW C02).
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Neumann, G. (2006). A Hybrid Machine Learning Approach for Information Extraction from Free Text. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_47
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DOI: https://doi.org/10.1007/3-540-31314-1_47
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