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研究生:趙啟雲
研究生(外文):CHI-YUN CHAO
論文名稱:整合聊天機器人與語音對話機器人: 打造K12數位學習助理
論文名稱(外文):E-Learning Assistant in K12 Academy: Integrating Chatbot and Voice Dialogue Robot
指導教授:吳曉光吳曉光引用關係
指導教授(外文):Hsiao-kuang Wu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:英文
論文頁數:48
中文關鍵詞:機器學習隱馬可爾科夫模型數位學習詞向量神經網路
外文關鍵詞:Machine learningE-Learningword2vec neural networkhidden Markovkov model
相關次數:
  • 被引用被引用:1
  • 點閱點閱:423
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
近年來數位學習越來越流行,因為數位學習有著能隨時隨地地進行學習的優點,相較於過去傳統教室老師對學生的教育學習者也可以針對自己的學習狀況,學生可以更加隨性調整學習進度,可以有效地增加學習上的效率。一個好的數位學習平台,線上老師客服是必不可少的,但由於龐大的使用客群,使用真人老師全天候的服務所有的學生成本非常高,因此我們開發了人工智慧教學助理系統,可以隨時隨地在社交通訊軟體上分擔線上老師客服的工作:為學生解決課業上的問題、解決學生數位平台的操作問題。為了更方便學生使用,我們甚至可以透過語音對話機器人,直接用語音對話方式為學生提供服務,提供更完善的數位學習環境。
本文提出的聊天機器人是一個檢索式模型,先藉由隱馬可爾科夫模型(Hidden Markov Model)將使用者的輸入分詞後,除去常用詞,提取出關鍵字,再將關鍵字透過詞向量化模型(Word2Vec Model)比對各個資料庫中定義好的對話主題的相似度,以此實做對使用者對話的關鍵字提取以及分類功能。
In recent years, E-Learning has become more and more popular because of being able to let students learn anytime and anywhere. Compared with traditional classroom learning, students can adjust their learning more casually according to their own learning conditions. It can increase the efficiency of their learning. For a good e-learning platform, on-line tutor service is need. But hiring human tutors will cost a lot due to large number of students in whole time use. Therefore, we develop a chatbot system to share some of the work of human tutors: solve problems in the course for students and provide FAQ of the operation in the e-learning platform. It can even provide service to students via voice dialogue robots. With our system, students can get a more complete e-learning environment.
The chatbot proposed in this paper is a retrieval-based model. After the user's input is segmented by the Hidden Markov Model to get the keywords, and the keywords are transmitted through the Word2vec model. The Word2Vec Model compares the similarity of the conversation topics defined in each database to achieve keyword extraction and classification for user conversations
摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures VI
1. Introduction 1
1.1 Motivation 1
1.2 Organization of Thesis 6
2. Related Works 7
2.1 E-Learning Research 7
2.2 Chatbot 11
2.3 Speech recognition 13
3. System Design 14
3.1 Background 14
3.2 System Architecture 15
3.2.1 State identify 19
3.2.2 Voice Recognition System 23
4. Implementation 24
4.1 Implementation Overview 24
4.2 Chat Bot System module 24
4.3 Web Server module 26
4.4 Voice Dialogue Robot module 26
4.5 Extend Chatbot Response Module 27
4.6 Training Data set for Word2Vec model 28
4.7 Results and Displays 29
5. Conclusion and Future Works 33
References 34
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