C4.5: programs for machine learning
C4.5: programs for machine learning
Automatic corpus-based Thai word extraction with the c4.5 learning algorithm
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Context-based spelling correction for Japanese OCR
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Non-dictionary-based Thai word segmentation using decision trees
HLT '01 Proceedings of the first international conference on Human language technology research
Multilingual OCR research and applications: an overview
Proceedings of the 4th International Workshop on Multilingual OCR
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The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.