Applying spelling error correction techniques for improving semantic role labelling

Authors:
Erik Tjong Kim Sang;Sander Canisius;Antal van den Bosch;Toine Bogers
Affiliations:
University of Amsterdam, SJ Amsterdam, The Netherlands;Tilburg University, LE Tilburg, The Netherlands;Tilburg University, LE Tilburg, The Netherlands;Tilburg University, LE Tilburg, The Netherlands
Venue:
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Year:
2005

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Abstract

This paper describes our approach to the CoNLL-2005 shared task: semantic role labelling. We do many of the obvious things that can be found in the other submissions as well. We use syntactic trees for deriving instances, partly at the constituent level and partly at the word level. On both levels we edit the data down to only the predicted positive cases of verb-constituent or verb-word pairs exhibiting a verb-argument relation, and we train two next-level classifiers that assign the appropriate labels to the positively classified cases. Each classifier is trained on data in which the features have been selected to optimize generalization performance on the particular task. We apply different machine learning algorithms and combine their predictions.