A Hybrid Machine Learning Approach for Information Extraction from Free Text

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.