• Smart Media Journal
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• v.12 no.9
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• pp.19-27
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• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.219-230
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• 2014

An analysis of dependency relation is a job that determines the governor and the dependent between words in sentence. The dependency relation of predicate is established by patterns and selectional restriction of subcategorization of the predicate. This paper proposes a method of analysis of Korean dependency relation using homograph predicate disambiguated in morphology analysis phase. The disambiguated homograph predicates has each different pattern. Especially reusing a stage transition training dictionary used during tagging POS and homograph, we propose a method of fixing the dependency relation of {noun+postposition, predicate}, and we analyze the accuracy and an effect of homograph for analysis of dependency relation. We used the Sejong Phrase Structured Corpus for experiment. We transformed the phrase structured corpus to dependency relation structure and tagged homograph. From the experiment, the accuracy of dependency relation by disambiguating homograph is 80.38%, the accuracy is increased by 0.42% compared with one of undisambiguated homograph. The Z-values in statistical hypothesis testing with significance level 1% is ${\mid}Z{\mid}=4.63{\geq}z_{0.01}=2.33$. So we can conclude that the homograph affects on analysis of dependency relation, and the stage transition training dictionary used in tagging POS and homograph affects 7.14% on the accuracy of dependency relation.