Khmer POS Tagger: A Transformation-based Approach with Hybrid Unknown Word Handling
Chenda Nou, Wataru Kameyama · International Conference on Semantic Computing (ICSC 2007) · 2007
This paper presents an initiative research on Khmer part-of-speech tagger. We propose some modifications on applying rule algorithm of the transformation-based approach to adapt to Khmer language which is morphologically and syntactically different from the English language. Furthermore, to overcome the limited coverage of the rule-based approach in handling unknown words, we propose a hybrid approach to combine the rule-based and trigram models. Although training on a very small corpus, both proposed approaches achieve higher accuracy than the conventional methods. The tagger achieves 95.27% on training data and 91.96% on test data which includes 9% of unknown words.