Peerachet Porkaew
Author directory2017
CASICT-DCU Neural Machine Translation Systems for WMT17
Jinchao Zhang | Peerachet Porkaew | Jiawei Hu | Qiuye Zhao | Qun Liu
Proceedings of the Second Conference on Machine Translation
Jinchao Zhang | Peerachet Porkaew | Jiawei Hu | Qiuye Zhao | Qun Liu
Proceedings of the Second Conference on Machine Translation
2014
Improvement of Statistical Machine Translation using Charater-Based Segmentationwith Monolingual and Bilingual Information
Vipas Sutantayawalee | Peerachet Porkaew | Prachya Boonkwan | Sitthaa Phaholphinyo | Thepchai Supnithi
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing
Vipas Sutantayawalee | Peerachet Porkaew | Prachya Boonkwan | Sitthaa Phaholphinyo | Thepchai Supnithi
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing
2010
A Supervised Learning based Chunking in Thai using Categorial Grammar
Thepchai Supnithi | Chanon Onman | Peerachet Porkaew | Taneth Ruangrajitpakorn | Kanokorn Trakultaweekool | Asanee Kawtrakul
Proceedings of the Eighth Workshop on Asian Language Resouces
Thepchai Supnithi | Chanon Onman | Peerachet Porkaew | Taneth Ruangrajitpakorn | Kanokorn Trakultaweekool | Asanee Kawtrakul
Proceedings of the Eighth Workshop on Asian Language Resouces
AutoTagTCG : A Framework for Automatic Thai CG Tagging
Thepchai Supnithi | Taneth Ruangrajitpakorn | Kanokorn Trakultaweekool | Peerachet Porkaew
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Thepchai Supnithi | Taneth Ruangrajitpakorn | Kanokorn Trakultaweekool | Peerachet Porkaew
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
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.