• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.665-668
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• 2018

본 논문에서는 제약기반 KBQA를 위한 질문분석 기술에 대해서 소개한다. 핵심개체와 속성에 대한 연결 모호성을 해소하기 위해서 세 종류의 제약정보 활용을 제안한다. 세 종류의 제약은 핵심개체에 기반한 제약, 의미정답유형에 기반한 제약, 속성단서에 기반한 제약이다. 제약을 위해서는 질문 내에서 핵심개체와 속성단서를 인식하여야 한다. 본 논문에서는 규칙과 휴리스틱에 기반한 핵심개체와 속성단서 인식 방법에 대해서 소개한다. 핵심개체와 속성단서 인식 실험은 구축된 229개의 질문을 대상으로 수행하였으며, 핵심개체와 속성단서가 모두 정확히 인식된 정확도(accuracy)가 57.21%이고, KBQA 대상질문에서는 71.08%를 보였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.750-762
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• 2019

The importance of the call center as a contact point for the enterprise is growing. However, call centers have difficulty with their operating agents due to the agents' lack of knowledge and owing to frequent agent turnover due to downturns in the business, which causes deterioration in the quality of customer service. Therefore, through an N-bank call center case study, we developed a system to reduce the burden of keeping up business knowledge and to improve customer service quality. It is a "real-time agent advisor" system that provides agents with answers to customer questions in real time by combining AI technology for speech recognition, natural language processing, and questions & answers for existing call center information systems, such as a private branch exchange (PBX) and computer telephony integration (CTI). As a result of the case study, we confirmed that the speech recognition system for real-time call analysis and the corpus construction method improves the natural speech processing performance of the query response system. Especially with name entity recognition (NER), the accuracy of the corpus learning improved by 31%. Also, after applying the agent advisor system, the positive feedback rate of agents about the answers from the agent advisor was 93.1%, which proved the system is helpful to the agents.