Word segmentation is a challenging preprocessing step for Thai Natural Language Processing due to the lack of explicit word boundaries. The previous systems rely on powerful neural network architecture alone and ignore linguistic substructures of Thai words. We utilize the linguistic observation that Thai strings can be segmented into syllables, which should narrow down the search space for the word boundaries and provide helpful features. Here, we propose a neural Thai Word Segmenter that uses syllable embeddings to capture linguistic constraints and uses dilated CNN filters to capture the environment of each character. Within this goal, we develop the first MLbased Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. Our word segmentation system outperforms the previous stateoftheart system in both speed and accuracy on both indomain and outdomain datasets.

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