Syllable-Based Multi-POSMORPH Annotation for Korean Morphological Analysis and Part-of-Speech Tagging - Hyeong Jin Shin; Jeongyeon Park; Jae Sung Lee (Applied Sciences, 2023)
Various research approaches have attempted to solve the length difference problem between the surface form and the base form of words in the Korean morphological analysis and part-of-speech (POS) tagging task. The compound POS tagging method is a popular approach, which tackles the problem using annotation tags. However, a dictionary is required for the post-processing to recover the base form and to dissolve the ambiguity of compound POS tags, which degrades the system performance. In this study, we propose a novel syllable-based multi-POSMORPH annotation method to solve the length difference problem within one framework, without using a dictionary for the post-processing. A multi-POSMORPH tag is created by combining POS tags and morpheme syllables for the simultaneous POS tagging and morpheme recovery. The model is implemented with a two-layer transformer encoder, which is lighter than the existing models based on large language models. Nonetheless, the experiments demonstrate that the performance of the proposed model is comparable to, or better than, that of previous models.