Summary of the paper
| Title | AutoTagTCG : A Framework for Automatic Thai CG Tagging |
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| Authors | Thepchai Supnithi, Taneth Ruangrajitpakorn, Kanokorn Trakultaweekool and 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. |
| Topics | LR Infrastructures and Architectures, Tools, systems, applications |
Full paper |
AutoTagTCG : A Framework for Automatic Thai CG Tagging |
Slides |
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| Bibtex | @InProceedings{SUPNITHI10.868, author = {Thepchai Supnithi and Taneth Ruangrajitpakorn and Kanokorn Trakultaweekool and Peerachet Porkaew}, title = {AutoTagTCG : A Framework for Automatic Thai CG Tagging}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |