This paper presents a novel ensemble learning approach to resolving German pronouns. Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. Experiments show that this approach is superior to a single decision-tree classifier. Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering and text summarisation. .
