A Comparison of Machine Learning Algorithms for Prepositional Phrase Attachment
Brian Mitchell (Department of Computer Science, University of Sheffield, Regent Court, Portobello Road, Sheffield. S1 4DP UK)
Robert Gaizauskas (Department of Computer Science, University of Sheffield, Regent Court, Portobello Road, Sheffield. S1 4DP UK)
WO23: Corpus Analysis, Annotation, Representation
This paper presents work which extends previous corpus-based work on training Machine Learning Algorithms to perform Prepositional Phrase attachment. Besides recreating othersí experiments to see how algorithmsí performance changes with the number of training examples and using n-fold cross-validation to produce more accurate error rates, we implemented our own vanilla Machine Learning Algorithms as a comparison. We also had people perform exactly the same task as the Machine Learning Algorithms to indicate whether the way forward lies in improving Machine Learning Algorithms or in improving the data sets used to train Machine Learning Algorithms. The results from all these experiments feed into our other work transforming the Penn TreeBank into a more useful resource for training Machine Learning Algorithms to do Prepositional Phrase attachment.
Prepositional phrase attachment