Syllable-Level Lightweight Korean POS Tagger using Transformer Encoder 


Vol. 13,  No. 10, pp. 553-558, Oct.  2024
https://doi.org/10.3745/TKIPS.2024.13.10.553


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  Abstract

Morphological analysis involves segmenting morphemes, the smallest units of meaning or grammatical function in a language, and assigning part-of-speech tags to each morpheme. It plays a critical role in various natural language processing tasks, such as named entity recognition and dependency parsing. Much of modern natural language processing relies on deep learning-based language models, and Korean morphological analysis can be broadly categorized into sequence-to-sequence methods and sequential labeling methods. This study proposes a morphological analysis approach using the transformer encoder for sequential labeling to perform syllable-level part-of-speech tagging, followed by morpheme restoration and tagging through a pre-analyzed dictionary. Additionally, the CBOW method was used to extract syllable-level embeddings in lower dimensions, designing a lightweight morphological analyzer model with reduced parameters. The proposed model achieves fast inference speed and low parameter usage, making it efficient for use in resource-constrained environments.

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[IEEE Style]

S. Min and Y. Ko, "Syllable-Level Lightweight Korean POS Tagger using Transformer Encoder," The Transactions of the Korea Information Processing Society, vol. 13, no. 10, pp. 553-558, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.10.553.

[ACM Style]

Suyoung Min and Youngjoong Ko. 2024. Syllable-Level Lightweight Korean POS Tagger using Transformer Encoder. The Transactions of the Korea Information Processing Society, 13, 10, (2024), 553-558. DOI: https://doi.org/10.3745/TKIPS.2024.13.10.553.

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