융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
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기계학습을 이용한 한국어 대화시스템 도메인 분류

Machine Learning Based Domain Classification for Korean Dialog System

  • 정영섭 (순천향대학교 빅데이터공학과)
  • 투고 : 2019.07.02
  • 심사 : 2019.08.20
  • 발행 : 2019.08.28
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초록

대화시스템은 인간과 컴퓨터의 상호작용에 새로운 패러다임이 되고 있다. 자연어로써 상호작용함으로써 인간은 보다 자연스럽고 편리하게 각종 서비스를 누릴 수 있게 되었다. 대화시스템의 구조는 일반적으로 음성 인식, 자연어 이해, 문맥 파악 등의 여러 모듈의 파이프라인으로 이뤄지는데, 본 연구에서는 자연어 이해 모듈의 도메인 분류 문제를 풀기 위해 convolutional neural network, random forest 등의 기계학습 모델을 비교하였다. 사람이 직접 태깅한 총 7개 서비스 도메인 데이터에 대하여 각 문장의 도메인을 분류하는 실험을 수행하였고 random forest 모델이 F1 score 0.97 이상으로 가장 높은 성능을 달성한 것을 보였다. 향후 다른 기계학습 모델들을 추가 실험함으로써 도메인 분류 성능 개선을 지속할 계획이다.

Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.

키워드

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Fig. 1. Pipeline process of dialog system

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Fig. 2. Distribution of the sentence length (i.e., the number of tokens in sentences)

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Fig. 3. CNN structure for domain classification

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Fig. 4. Random Forest

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Fig. 5. Precision of comparable models

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Fig. 6. Recall of comparable models

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Fig. 7. F1 score of comparable models

Table 1. Data samples used for experiments

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Table 2. Data statistics

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Table 3. Weighted performance of comparable models

JKOHBZ_2019_v9n8_1_t0003.png 이미지

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