AutoTagTCG : A Framework for Automatic Thai CG Tagging

Thepchai Supnithi, Taneth Ruangrajitpakorn, Kanokorn Trakultaweekool, Peerachet Porkaew

Abstract
This paper aims to develop a framework for automatic CG tagging. We investigated two main algorithms, CRF and Statistical alignment model based on information theory (SAM). We found that SAM gives the best results both in word level and sentence level. We got the accuracy 89.25% in word level and 82.49% in sentence level. Combining both methods can be suited for both known and unknown word.
Anthology ID:
L10-1599
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/868_Paper.pdf
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Cite (ACL):
Thepchai Supnithi, Taneth Ruangrajitpakorn, Kanokorn Trakultaweekool, and Peerachet Porkaew. 2010. AutoTagTCG : A Framework for Automatic Thai CG Tagging. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
AutoTagTCG : A Framework for Automatic Thai CG Tagging (Supnithi et al., LREC 2010)
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http://www.lrec-conf.org/proceedings/lrec2010/pdf/868_Paper.pdf
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