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Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, 'off-the-shelf', parts-of-speech tagging tools
This paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.
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Event
International Symposium of the Pattern Recognition Association of South Africa (18th : 2007 : Pietermaritzburg, South Africa)Pagination
19 - 24Publisher
PRASALocation
Pietermaritzburg, South AfricaPlace of publication
Durban, South AfricaStart date
2007-11-28End date
2007-11-30ISBN-13
9781868406562Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2007, PRASAEditor/Contributor(s)
J Tapamo, F NicollsTitle of proceedings
PRASA 2007 : Proceedings of the 18th International Symposium of the Pattern Recognition Association of South AfricaUsage metrics
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