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)
  • 1