Abstract
The development of systems which can be easily adapted to new domains
is an important goal in current Information Extraction (IE) research.
Machine learning algorithms have been applied to the problem but
supervised algorithms often require large amounts of examples and
unsupervised ones may be hampered by a lack of information. This
paper presents an unsupervised algorithm which makes use of the
WordNet ontology to compensate for the small number of examples.
Comparative evaluation with a previously reported approach shows
that the algorithm presented here is in some ways preferable and
that benefits can be gained from combining the two approaches.
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