Semantic Role Labelling With Chunk Sequences
Ulrike Baldewein, Katrin Erk, Sebastian Padó, Detlef Prescher · 2004
We describe a statistical approach to semantic role labelling that employs only shallow infor-mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument can-didate. The instances to be classified are se-quences of chunks that occur frequently as ar-guments in the training corpus. Our best model obtains an F score of 51.70 on the test set. 1