While being successful in providing keyword based access to web pages, commercial search portals still lack the ability to answer questions expressed in a natural language. We present a probabilistic approach to automated question answering on the Web, based on trainable patterns, answer triangulation and semantic filtering. In contrast to the other “shallow ” approaches, our approach is entirely self-learning. It does not require any manually created scoring and filtering rules while still performing comparably. It also performs better than other fully trainable approaches.