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.

Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction.The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4% point increase in spatial element extraction and an 8.2% point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).