- Title
- Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, off-the-shelf, parts-of-speech tagging tools reviewed article
- Creator
- Glass, Kevin R, Bangay, Shaun D
- Subject
- To be catalogued
- Date
- 2008
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/433427
- Identifier
- vital:72969
- Identifier
- https://hdl.handle.net/10520/EJC28053
- Description
- 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.
- Format
- 7 pages, pdf
- Language
- English
- Relation
- South African Computer Journal, Glass, K. and Bangay, S., 2008. Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained," off-the-shelf", parts-of-speech tagging tools: reviewed article. South African Computer Journal, 2008(40), pp.4-10, South African Computer Journal volume 2008 number 40 4 10 2008 2313-7835
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the Sabinet Terms and Conditions Statement (https://www.sabinet.co.za/terms-conditions)
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Collections
Bangay, Shaun Douglas (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
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