My research focuses on the application of machine learning techniques to natural language processing tasks. Primarily, I am working on question answering (QA), the task of retrieving precise answers to natural language questions from a document collection.

I am the author of the Ephyra QA system, which I developed at Universität Karlsruhe (TH) and later at Carnegie Mellon University. Ephyra is a modular and extensible framework that facilitates the integration of different QA techniques. The system is organized as a pipeline of reusable standard components for question analysis, query generation, search, answer extraction, and answer selection. The most recent setup combines a syntactic pattern learning and matching approach with answer-type based extraction techniques and a semantic extractor that is based on semantic role labeling.

Currently I am working on OpenEphyra, the first open source question answering system, intended to facilitate the collaboration in the field. The goal is to give researchers the opportunity to develop new QA techniques without worrying about the end-to-end system. I also believe that OpenEphyra can facilitate evaluations and comparisons of different approaches by providing a common platform for experiments. In addition, OpenEphyra can be used for educational purposes, such as for computer science course projects. Please take a look at the Ephyra website for more information about this project, or visit the SourceForge project site to download the latest release.