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Semantic-Based Emotional Inference and Agent Interaction Applied in Education

Semantic-Based Emotional Inference and Agent Interaction Applied in Education. AUTHORS: I-HEN TSAI 1 , RUI-TING SUN 1 , REN-YING FANG 1 , KOONG H.-C. LIN 1 , MIN-CHAI SHIEH 1 , JIUN-SHENG LI 2 , CHU-CHUAN HUANG 2 , JHING-FA WANG 3 1 NATIONAL UNIVERSITY OF TAINAN

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Semantic-Based Emotional Inference and Agent Interaction Applied in Education

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  1. Semantic-Based Emotional Inference and Agent Interaction Applied in Education AUTHORS: I-HEN TSAI1, RUI-TING SUN1 , REN-YING FANG1, KOONG H.-C. LIN1, MIN-CHAI SHIEH1, JIUN-SHENG LI2, CHU-CHUAN HUANG2, JHING-FA WANG3 1NATIONAL UNIVERSITY OF TAINAN 2HCI TECHNOLOGY CENTER, ITRI 3NATIONAL CHENG KUNG UNIVERSITY PRESENTER: I-HEN TSAI

  2. Overview • Abstract: In a nutshell • Introduction • System Design • Putting together the pieces • Experimentation • Results • Conclusion • Future Works

  3. Abstract • A system for student interaction in education environment • Text input • Inference emotion from text • Text to agent for visual interaction

  4. Introduction • System to interact with students • Improve concentration • Bring student attention back to class • Based on analyzing text • Emotion • What they think of the class

  5. Semantic Analysis • Semantic information extraction • Ontology approach • Connection between concepts • OMCSnet • Text string input • Output term relation values with target concepts • Common sense inference • Dogs <bark> not <meow> • Mapping rules: attributes + operations • Predicates database: 250000 items

  6. Simplified Algorithm: Sentence Parsing • Data structure requirement: • 2 dequeues, digestedSymbol & digestedToken • 2 stacks, symbolDequeue & tokenDequeue • For each token from argv[1] to argv[n] • On [* : push * into symbolDequeue, push an empty string into tokenDequeue. • If currentToken is [NP, skip to the corresponding NP] • On *] : push symbolDequeue.top() into “digestedSymbol,” push tokenDequeue.top() into “digestedToken.” • On * : Append currentString in tokenDequeue.top()

  7. Algorithm: Emotional Inference • Define I[] = set of tokens translated into English. • Define E[]= { concentrate, happy, relax, easy }, • emotions[sizeof(I)][4]; • pathSum = 0; • for each I[i] • for each E[j] • Let D[i][j] = distance( I[i], E[j] ) • pathSum += D[i][j] • end of for • for each E[j] • emotions[i][j] = (pathSum - D[i][j])/pathSum • end of for • pathSum=0 • end of for

  8. Process Flow of Semantic Analysis

  9. Agent • Visual avatar • 偽春菜 or “ukagaka” (伺か) • C-based system • Varied interaction capabilities • Can be user defined to suit need • Can script wanted dialogue • Interchangeable skin

  10. System Flow • Text input • Translation • Parse text to pick out significant terms • Match sig. terms with target concepts for term relation value • Determine concentration value • Pass result to agent • Agent picks dialogue based on received value

  11. System Process Flow

  12. Experiment Setup • Educational domain • Educational institutional background and direction • The speech occurrences of student chatter in class • Translation • Balanced sets

  13. Results • Main concept: concentration • Indicators: happy, easy, relax • Main concept triggers interaction • Indicators allow the viewer to have some insight of what students think of the class

  14. Conclusions • Main issues: • Translation issues • OMCSnet • Concept search must be perfect match, eg. <concentrate> does not equal <concentrated> • Lack of desired link between concept • Shortest path first • Agent • Needs feedback mechanism

  15. Future works • Better translation alternative(?) • OMCSnet • Weighting system(?), in attempt • Alternate ontology mapping structure • Add more items • Agent • Feedback mechanism • Interaction affects future inference • Other multimedia outputs

  16. Thank You for your attention!

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