@article{MAD6D3472,
title = "Korean Morphological Analysis Method
Based on BERT-Fused Transformer Model",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2022",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2022.11.4.169",
author = "Changjae Lee/Dongyul Ra",
keywords = "Natural Language Processing, Morphological Analysis, Transfer Learning, Transformer, BERT-fused Model",
abstract = "Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean,
a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological
analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate
part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing
(NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA
has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain
into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit
neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into
a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model
is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer,
a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance
in NLP. The experimental results show that our model achieves 98.24 F1-Score."
}