지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 329 - 349 (21page)
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초록· 키워드
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The purpose of this study is to test models that automatically classify Korean nouns into native Korean, Sino-Korean, and loanwords by applying a machine learning model, naïve Bayes classification. In this study, 500 native Korean words, Sino-Korean words, and loanwords were collected, and after romanizing and decomposing them into bigram and trigram lists, the bigrams and trigrams were entered into the naïve Bayes classifier. We tested models with and without syllable boundaries, and found that both the bigram and trigram models were over 80% accurate. Contrary to the expectation that the performance of the models would improve as more information about Korean phonotactics was included in the training and validation data, the difference in performance between the bigram and trigram models was not significant. The model that included syllable boundaries in the phoneme sequence information had slightly higher accuracy than the model without syllable boundary information. When comparing the classification results of all five models, the accuracy of the bigram model with syllable boundaries was 83.55%, which was the best. For now, we have modified the model to consider only phoneme sequence information and syllable boundaries, but it is expected that the accuracy of the model can be improved by training the model while excluding bigrams and trigrams, which occur in similar proportions in all categories, and by increasing the size of the data.
목차
Abstract
1. 머리말
2. 한국어의 어휘 계층론과 음소배열 정보
3. 데이터의 수집과 전처리
4. 나이브 베이즈 분류
5. 고유어, 한자어, 차용어 분류 모델의 평가
6. 맺음말
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