Earticle

초록

영어

This study aims to describe music-related request patterns observed in AI assistant platforms and formalize them by using the local grammar graph(LGG) methodology. These patterns, transformed into Finite-State Transducers in the Unitex platform, allow us to automatically classify and annotate various sentences used for request, which is crucial for diverse machine learning approaches. In this study, we examined a corpus of tweet texts crawled by Deco-T-Crawler as seed data and classified music-related requests into 3 categories: music on/off, volume up/down and music-play control. Then each category was divided into sub-categories which were represented by several LGGs. By applying these LGGs and the DECO Korean dictionary on test data, we obtained the information retrieval performance. In this regard, the linguistic resources proposed in this study are proved concrete and meaningful.

목차

Abstract
1. 머리말
2. 선행연구
3. 언어자원 구축 방법론
3.1. AI 어시스턴트 플랫폼과 하위 카테고리 분류
3.2. 데이터 수집 및 자원 구축 플랫폼
3.3. 음악 청취 도메인의 카테고리별 문형구조의 특징
4. AI 어시스턴트를 위한 LGG 언어자원의 구축
4.1. 개체명 인식을 위한 LGG 구성
4.2. 용언과 보조 용언 활용을 인식하기 위한 LGG 구성
4.3. 자원 호출 그래프 및 태깅 시퀀스
5. 성능 평가
6. 결론
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저자

• 황창회 [ Changhoe Hwang | 한국외국어대학교/대학원생 ] 주저자
• 윤소은 [ Soeun Yun | 한국외국어대학교/대학원생 ] 공동저자
• 남지순 [ Jeesun Nam | 한국외국어대학교/교수 ] 교신저자

참고문헌