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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77521

    Title: 實作AI聊天機器人於校園諮詢服務之研究;A Study of Implementing AI Chatbot in Campus Consulting Service
    Authors: 陳昭安;Chen, Chao-An
    Contributors: 資訊工程學系在職專班
    Keywords: 聊天機器人;自然語言學習;客服服務;即時通訊;Chatbot;NLP;Customer Service;Instant Message
    Date: 2018-08-23
    Issue Date: 2018-08-31 14:46:51 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 近年來因社群媒體與即時通訊軟體的普及,使得行動訊務量快速增加,透過手機聊天已成為人與人之間最簡單且最直接的互動方式,因此造就聊天機器人此種新的服務型態。透過與機器人簡單的對話來獲得資訊或是藉由機器人將工作既定的流程簡化來降低資源成本,已成為業界研究的熱門主題。聊天機器人(Chatbot)是經由對話或文字進行交談的電腦程式,藉由自然語言的學習與訓練能夠模擬人類對話,給予適當的回饋資訊。
    本論文研究主要以Python為程式開發語言,利用開源的網頁服務框架Django、微軟QnA Maker與LUIS自然語意辨識服務來設計與建構聊天機器人。以往建立聊天機器人必須熟知自然語言和海量語料資料的訓練才能得到有效的成果,此系統重點著重在節省資料訓練的時間及快速部署校園諮詢機器人客服系統,利用現有的校園FAQ網站與問題集,透過自動化檢索與訓練來實作機器人客服,強化既有工作流程。
    ;Due to popularize of social networks and instant messaging applications, mobile usage has grown at an unprecedented pace and has become the most efficient way for people to connect. As a result, sparked a new type of service through automated conversational agents, Chatbot. Lowering costs by disseminating information through predesigned processes has become a hot industry research topic. A chatbot is an automated response system through texts or voice messaging, achieved by artificial learning to simulate human communication.
    This research based on Python for program development utilizes open source web framework Django, Microsoft’s QnA Maker and LUIS for text recognition to design chatbot. Previous chatbot developments require proficient natural language cognition and training through massive language information to reach effective results. This system, however, focuses on minimizing information training time to achieve quicker implementation of in-school information chatbot system; by gathering school’s FAQ pages and threads, automated collection and training will strengthen chatbot’s work processes for effective services.
    Other than developing chatbot services, this research also suggests managing protocols, including chat logs, inquiring session logs, service quality feedbacks, transference to person and rating feedbacks. Through simplified management and training, visualized monitoring tools, data analysis, backend operator can grasp the effectiveness of interactions in real-time, making sure quality fits its entirety. Most importantly, alleviate servicing staff’s workload, accumulate experience and data, resulting in improvement of the service quality that reaches to 12.63%.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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