Pattarawat Chormai


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PyThaiNLP: Thai Natural Language Processing in Python
Wannaphong Phatthiyaphaibun | Korakot Chaovavanich | Charin Polpanumas | Arthit Suriyawongkul | Lalita Lowphansirikul | Pattarawat Chormai | Peerat Limkonchotiwat | Thanathip Suntorntip | Can Udomcharoenchaikit
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)

We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at


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Syllable-based Neural Thai Word Segmentation
Pattarawat Chormai | Ponrawee Prasertsom | Jin Cheevaprawatdomrong | Attapol Rutherford
Proceedings of the 28th International Conference on Computational Linguistics

Word segmentation is a challenging pre-processing 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 ML-based Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. Our word segmentation system outperforms the previous state-of-the-art system in both speed and accuracy on both in-domain and out-domain datasets.