Abstract
We present and evaluate the implementation of Part of Speech (POS) Tagging for the Kadazan language by using the Transformation-based approach. The main purpose of this study is to develop an automatic POS tagging for the Kadazan language, which had never, been developed before. POS tagging can tag the Kadazan corpus automatically and can help reduce the disambiguation problem of this language. The implementation of this approach in this study is to achieve a better and higher accuracy or at least similar to that of the other tagging approaches such as the statistical and the original rule-based approach. This approach can transform the tags based on the prescribed set of rules. A number of objectives were set in order to achieve the main purpose of this study. Firstly, to apply the lexical and contextual rules for this language. Secondly, to implement the Brill's algorithm based on the set of rules and finally to determine the effectiveness of the Kadazan Part of Speech by using this approach. The tagging system had been trained using four Kadazan corpuses containing 5663 words in all. Based on the evaluation results, the tagging system had achieved around 93% accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 177-190 |
| Number of pages | 14 |
| Journal | Journal of ICT Research and Applications |
| Volume | 7 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2013 |
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Keywords
- Brill's tagger
- Kadazan language
- Part of Speech tagger
- Rule-based
- Statistical
- Transformation-based
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering
- Information Systems and Management
Cite this
Implementation of Kadazan Tagger Based on Brill's Method. / Alex, Marylyn; Zakaria, Lailatul Qadri.
In: Journal of ICT Research and Applications, Vol. 7, No. 3, 2013, p. 177-190.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Implementation of Kadazan Tagger Based on Brill's Method
AU - Alex, Marylyn
AU - Zakaria, Lailatul Qadri
PY - 2013
Y1 - 2013
N2 - We present and evaluate the implementation of Part of Speech (POS) Tagging for the Kadazan language by using the Transformation-based approach. The main purpose of this study is to develop an automatic POS tagging for the Kadazan language, which had never, been developed before. POS tagging can tag the Kadazan corpus automatically and can help reduce the disambiguation problem of this language. The implementation of this approach in this study is to achieve a better and higher accuracy or at least similar to that of the other tagging approaches such as the statistical and the original rule-based approach. This approach can transform the tags based on the prescribed set of rules. A number of objectives were set in order to achieve the main purpose of this study. Firstly, to apply the lexical and contextual rules for this language. Secondly, to implement the Brill's algorithm based on the set of rules and finally to determine the effectiveness of the Kadazan Part of Speech by using this approach. The tagging system had been trained using four Kadazan corpuses containing 5663 words in all. Based on the evaluation results, the tagging system had achieved around 93% accuracy.
AB - We present and evaluate the implementation of Part of Speech (POS) Tagging for the Kadazan language by using the Transformation-based approach. The main purpose of this study is to develop an automatic POS tagging for the Kadazan language, which had never, been developed before. POS tagging can tag the Kadazan corpus automatically and can help reduce the disambiguation problem of this language. The implementation of this approach in this study is to achieve a better and higher accuracy or at least similar to that of the other tagging approaches such as the statistical and the original rule-based approach. This approach can transform the tags based on the prescribed set of rules. A number of objectives were set in order to achieve the main purpose of this study. Firstly, to apply the lexical and contextual rules for this language. Secondly, to implement the Brill's algorithm based on the set of rules and finally to determine the effectiveness of the Kadazan Part of Speech by using this approach. The tagging system had been trained using four Kadazan corpuses containing 5663 words in all. Based on the evaluation results, the tagging system had achieved around 93% accuracy.
KW - Brill's tagger
KW - Kadazan language
KW - Part of Speech tagger
KW - Rule-based
KW - Statistical
KW - Transformation-based
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U2 - 10.5614/itbj.ict.res.appl.2013.7.3.1
DO - 10.5614/itbj.ict.res.appl.2013.7.3.1
M3 - Article
VL - 7
SP - 177
EP - 190
JO - Journal of ICT Research and Applications
JF - Journal of ICT Research and Applications
SN - 2337-5787
IS - 3
ER -