Earticle

초록

영어

This research delves into linguistic patterns within a medical QA corpus focused on skin diseases. Our primary goal is to analyze these patterns and establish linguistic resources for the automated extraction of diagnostic information, notably disease and symptom expressions. A thorough examination of the corpus revealed three key linguistic patterns in user utterances: varied disease and symptom descriptions, specific query-related linguistic structures, and expressions giving supplementary background information. From these patterns, we classified 12 distinct query types. Furthermore, we identified three vital query-related expressions concerning skin diseases: WHAT, WHY, and HOW-CURE. These linguistic patterns were encapsulated using the Local Grammar Graph (LGG) schema, designed to efficiently produce training datasets for medical chatbots' Natural Language Understanding (NLU) modules. Validating our approach, a medical counseling chatbot named LIMA, trained using our dataset, achieved an F1-score of 0.908, underscoring the effectiveness and reliability of our proposed method.

목차

Abstract
1. 서론
2. 선행 연구
2.1. 의료 도메인 챗봇 관련 연구
2.2. 한국어 증상 표현 관련 연구
3. 의료상담 질의문 유형 및 패턴 분석
3.1. 의료상담 질의문의 의미적 특징
3.2. 의료상담 질의문의 형식적 구조
4. 질의유형별 언어자원 구축
4.1. 질병 표현 그래프 자원
4.2. 질의 화행 표현 그래프 자원
4.3. 부가 표현 그래프 자원
5. 성능 평가
6. 결론 및 향후 연구
참고문헌

키워드

의료상담 챗봇 자연언어이해 언어자원 질병 및 의학증상 표현 질의 화행 표현 부분문법그래프(LGG) medical counseling chatbot Natural Language Understanding(NLU) linguistic resources disease and medical symptoms query-related expressions Local grammar graph(LGG)

저자

• 박온유 [ Onyu Park | 한국외국어대학교/대학원생 ] 제1저자
• 남지순 [ Jeesun Nam | 한국외국어대학교/교수 ] 교신저자

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