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A Hybrid Machine Learning Approach for Information Extraction from Free Texts.

Günter Neumann
In: M. Spiliopoulou; R. Kruse; C. Borgelt; A. Nürnberger; W. Gaul (Hrsg.). From Data and Information Analysis to Knowledge Engineering. Pages 390-397, Studies in Classification, Data Analysis, and Knowledge Organization, ISBN 3-540-31313-3, Springer-Verlag Berlin, Heidelber, New-York, 2006.


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.