Ulrike Baldewein

Learn More
We describe a system for semantic role assignment built as part of the Senseval III task, based on an off-the-shelf parser and Maxent and Memory-Based learners. We focus on generalisation using several similarity measures to increase the amount of training data available and on the use of EM-based clustering to improve role assignment. Our final score is(More)
We describe a statistical approach to semantic role labelling that employs only shallow information. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument candidate. The instances to be classified are sequences of chunks that occur frequently as arguments in the training corpus. Our best model(More)
We describe an incremental, two-stage probabilistic model of human parsing for German. The model is broad coverage, i.e., it assigns sentence structure to previously unseen text with high accuracy. It also makes incremental predictions of the attachment decisions for PP attachment ambiguities. We test the model against reading time data from the literature(More)
  • 1