Unsupervised semantic role labelling. Swier, R. & Stevenson, S. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), pages 95–102, Barcelona, Spain, July, 2004. abstract bibtex We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unambiguous role assignments and then iteratively updates the probability model on which future assignments are based. A novel aspect of our approach is the use of verb, slot, and noun class information as the basis for backing off in our probability model. We achieve 50–65% reduction in the error rate over an informed baseline, indicating the potential of our approach for a task that has heretofore relied on large amounts of manually generated training data.
@InProceedings{ swier1,
author = {Robert Swier and Suzanne Stevenson},
title = {Unsupervised semantic role labelling},
booktitle = {Proceedings of the 2004 Conference on Empirical Methods in
Natural Language Processing (EMNLP 2004)},
pages = {95--102},
address = {Barcelona, Spain},
month = {July},
year = {2004},
abstract = {We present an unsupervised method for labelling the
arguments of verbs with their semantic roles. Our
bootstrapping algorithm makes initial unambiguous role
assignments and then iteratively updates the probability
model on which future assignments are based. A novel aspect
of our approach is the use of verb, slot, and noun class
information as the basis for backing off in our probability
model. We achieve 50--65% reduction in the error rate over
an informed baseline, indicating the potential of our
approach for a task that has heretofore relied on large
amounts of manually generated training data.},
download = {http://ftp.cs.toronto.edu/pub/gh/swier-stevenson-emnlp04.pdf}
}
Downloads: 0
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