한의학 고문헌 데이터 분석을 위한 단어 임베딩 기법 비교: 자연어처리 방법을 적용하여

• 영문명: Comparison between Word Embedding Techniques in Traditional Korean Medicine for Data Analysis: Implementation of a Natural Language Processing Method
• 발행기관: 대한한의학원전학회
• 저자명: 오준호(Oh Junho)
• 간행물 정보: 『대한한의학원전학회지』32권 1호, 61~74쪽, 전체 14쪽
• 주제분류: 의약학 > 한의학
• 파일형태: PDF
• 발행일자: 2019.02.28

영문 초록

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a prescription generating game and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

목차

I. 서론

Ⅱ. 본론

Ⅲ. 결론

키워드

Word embedding East Asian traditional medicine Korean Medicine data analysis natural language processing