本研究建構一自動化的對話分類系統，使其能將國小學童所進行之電腦合作學習活動的對話內容，依照已定義的對話行為類型進行自動分類，包括如｢討論概念｣、｢討論命題｣、｢整理階層關係｣、｢要求幫助或提供幫助｣、｢檢討構圖任務的正確性｣、｢時間控制｣、｢流程控制｣、｢協調操作權責｣、｢計較權責｣、｢激勵組員｣、｢確認組員是否在線上｣、｢詢問組員身分｣、｢搗亂｣、｢吵嘴｣、｢其它事件｣等十五類的對話行為類型。此系統先將對話內容進行斷詞，經由本體論中相關權重運算之後，能將對話內容歸類到各所屬的對話行為類型。為了測試該系統的分類效果，將電腦所分類出的結果與人工分析結果進行比對。研究結果發現：（a）當該系統在分類｢檢討構圖任務的正確性｣及｢流程控制｣的對話行為類型時的結果較佳，但在分類｢詢問組員身分｣、｢其它事件｣的對話行為類型時的結果較差；（b）系統針對無完整結構的句子、需從溝通脈絡判斷對話行為類型的句子以及有錯別字的句子容易分類錯誤，建議未來設計應能考量加入錯別字的校正以及對話內容脈絡的處理。

An automatic classification system for primary school students' dialogues was developed in the study. The system categorized participants’ dialogues obtained from a study on computer supported collaborative learning based on 15 types of dialogue acts, including “concept”, “proposition”, “hierarchy”, “for help and help”, “review”, “time controlling”, “flow controlling”, “conflict dealing”, “accountability demanding”, “motivating”, “name calling”, “disturbing”, “bicker” and “others”, by word segmentation, weighting calculation and classification from the perspective of ontology. The classification produced by the system was compared with the classification made by the experts in order to examine the effectiveness of the system. The study result revealed that (a) dialogues related to the dialogue act “review” and “flow controlling” could be classified more properly by the system than dialogues belonged to the dialogue act “name calling” and “others”; and (b) sentences, without structures or with misspelled words, and dialog contained context tended to be classified improperly by the system. Therefore, an advanced system with grammatical and spelling check and detection of context was recommended for the future development.

Avancini, H., Lavelli, A., Sebastiani, F., & Zanoli, R. (2006). Automatic expansion of domain-specific lexicons by term categorization. ACM Trans. Speech Lang. Process., 3(1), 1-30.
Baker, M., Andriessen, J., Lund, K., Amelsvoort, M. v., & Quignard, M. (2007). Rainbow: A framework for analysing computer-mediated pedagogical debates. International Journal of Computer-Supported Collaborative Learning, 2, 315-357.
Barros, B., & Verdejo, M. F. (2000). Analysing student interaction processes in order to improve collaboration. International Journal of Artificial Intelligence in Education, 11, 221-241.
Bloehdorn, S., & Hotho, A. (2004). Boosting for text classification with semantic features. In Proceedings of Proceedings of the MSW 2004 Workshop at the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 70-87), Seattle, WA, USA.
Chiu, C.-H. (2003). Exploring how primary school students function in computer supported collaborative learning. Engineering Education and Lifelong Learning, 13(3/4), 258-267.
Cohen, W. W., Carvalho, V. R., & Mitchell, T. M. (2004). Learning to classify email into "Speech acts". In Proceedings of Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain.
Dönmez, P., Rosé, C., Stegmann, K., Weinberger, A., & Fischer, F. (2005). Supporting cscl with automatic corpus analysis technology. In Proceedings of Proceedings of th2005 conference on computer support for collaborative learning (pp. 125-134), Taipei, Taiwan. International Society of the Learning Sciences
Estival, D., Nowak, C., & Zschorn, A. (2004, July). Towards ontology-based natural language processing. In Proceedings of Proceedings of the 4th Workshop on NLP and XML (NLPXML-2004) (pp. 59-66), Barcelona, Spain. Association for Computational Linguistics.
Feng, D., Shaw, E., Kim, J., & Hovy, E. (2006). Learning to detect conversation focus of threaded discussions. In Proceedings of Human Language Technology Conference of the North American Chapter ACL (pp. 208-215), New York, USA.
Fišel, M. (2007). Machine learning techniques in dialogue act recognition. Estonian Papers in Applied Linguistics(3/2007), 117-134.
Goodman, B. A., Linton, F. N., Gaimari, R. D., Hitzeman, J. M., Ross, H. J., & Zarrella, G. (2006). Using dialogue features to predict trouble during collaborative learning. User Modeling and User-Adapted Interaction, 16(1), 83-84.
Grüninger, M., & Fox, M. S. (1995). Methodology for the design and evaluation of ontologies. Paper presented at the Proceedings of the Fourth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. Berkeley Springs, West Virginia, USA.
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A., et al. (2004). Auto tutor: A tutor with dialogue in natural language. Behavior Research Methods, 36, 180-192.
Grau, S., Sanchis, E., Castro, M. ı. J. e., & Vilar, D. (2004). Dialogue act classification using a bayesian approach. In Proceedings of The 9th Conference Speech and Computer, St. Petersburg, Russia.
Grothkast, A., Adrian, B., Schumacher, K., & Dengel, A. (2008). Ocas: Ontology-based corpus and annotation scheme. In Proceedings of Proceeding of the high-level information extraction workshop (pp. 25-35), Antwerpen, Belgium.
Gruber, T. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human Computer Studies, 43, 907-928.
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Appeared in Knowledge Acquisition, 5(2), 199-220.
Hendler, J. (2001). Agents and the semantic web. IEEE INTELLIGENTSYSTEMS, 16(2), 30-37.
Holsti, O. R. (1969). Content analysis for social sciences and humanities. Reading: MA: Addison-Wesley.
Keet, C. M. (2004). Aspects of ontology integration. Napier University, Scotland.
Khan, L., McLeod, D., & Hovy, E. (2005). A framework for effective annotation of information from closed captions using ontologies. The Journal of Intelligent Information Systems, 25(2), 181-205.
Lazonder, A. W., Wilhelm, P., & Ootes, S. (2003). Using sentence openers to foster student interaction in computer-mediated learning environments. Computers & Education, 40(3), 291-308.
Levin, L., Langley, C., Lavie, A., Gate, D., Wallance, D., & Peterson, K. (2003). Domain specific speech acts for spoken language translation. Paper presented at the Proceedings of the 4th SIGdial workshop on discourse and dialogue Sappo, Japan.
Louwerse, M., & Crossley, S. (2006). Dialogue act classification using n-gram algorithms. In Proceedings of the 19th internation Florida artifical intelligence research society, Melbourne Beach, FL.
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
Naidu, S., & Jarvela, S. (2006). Analyzing cmc content for what. Computers & Education, 46, 96-103.
Natalya, F. N., & Deborah, L. M. (2001). Ontology development 101: A guide to creating your first ontology. Palo Alto, California, USA: Stanford University.
Niekrasz, J., & Purver, M. (2006). A multimodal discourse ontology for meeting understanding. Machine Learning for Multimodal Interaction, 3869, 162-173.
Orvis, K. L., Wisherb, R. A., Bonkc, C. J., & Olson, T. M. (2002). Communication patterns during synchronous web-based military training in problem solving. Computers in Human Behavior, 18, 783-795.
Pena-Shaff, J., Martin, W., & Gay, G. (2001). An epistemological framework for analyzing student interactions in computer-mediated communication environmenst. Journal of Interactive Learning Research, 12(1), 41-68.
Pilkington, R. (2001). Analysing education dialogue interaction: Towards models that support learning. International Journal of Artificial Intelligence in Education, 12, 1-7.
Quinlan, J. R. (1979). Induction over large data bases. Stanford, CA, USA.
Ricardo, B.-Y., & Berthier, R.-N. (1999). Modern information retrieval. New York: Person.
Rosé, C., Wang, Y.-C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., et al. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. Computer-Supported Collaborative Learning, 3, 237–271.
Saias, J., & Quaresma, P. (2003). A methodology to create ontology-based information retrieval systems. Natural language and Texxt Retrival, 290, 424-434.
Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York, USA: McGraw-Hill.
Shaw, W. M., Burginb, R., & Howella, P. (1997). Performance standards and evaluations in ir test collections: Cluster-based retrieval models Information Processing & Management, 33(1), 1-14.
Sinica, A. (1998). Chinese knowlege information processing "Cikp auto tag". from http://godel.iis.sinica.edu.tw/CKIP/index.htm
Soller, A. (2004). Computational modeling and analysis of knowledge sharing in collaborative distance learning. User Modeling and User-Adapted Interaction, 14, 351-381.
Soller, A., Lesgold, A., Linton, F., & Goodman, B. (1999). What makes peer interaction affective? Modeling affective communication in an intelligent cscl. Paper presented at the the 1999 AAAI Fall Symposium: Psychological Models of Communication in Collaborative Systems. Cape-Cod, MA.
Soller, A., Wiebe, J., & Lesgold, A. (2002). A machine learning approach to assessing knowledge sharing during collaborative learning activities. In Proceedings of Proceedings of computer-support for collaborative learning, New Jersey, USA.
Tho, Q. T., Hui, S. C., Fong, A. C. M., & Cao, T. H. (2006). Automatic fuzzy ontology generation for semantic web. IEEE Transactions on Knowledge and Data Engineering, 18, 842-856.
Uschold, M., & Grüninger, M. (1996). Ontologies: Principles, methods and applications. The knowledge engineering review, 11(2), 1-61.
Yang, S.-Y. (2009). Developing of an ontological interface agent with template-based linguistic processing techique for faq services. Expert Systems with Applications, 36, 4049-4060.
Zadrozny, W. (1995, Aug). Context and ontology in understanding of dialogs. In Proceedings of IJCAI'95 Workshop on Context in NLP., Montreal.