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

Günter Neumann
A Hybrid Machine Learning Approach for Information Extraction from Free Texts.
in: M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, W. Gaul (eds.):
1 From Data and Information Analysis to Knowledge Engineering,
Studies in Classification, Data Analysis, and Knowledge Organization, Pages 390-397, 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.
 
Files: BibTeX, GN-GfKL005-final.pdf