emnlp emnlp2010 emnlp2010-26 emnlp2010-26-reference knowledge-graph by maker-knowledge-mining
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Author: Su Nam Kim ; Lawrence Cavedon ; Timothy Baldwin
Abstract: We explore the task of automatically classifying dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investigate the effectiveness of various features and machine learners for this task. While a simple bag-of-words approach provides a solid baseline, we find that adding information from dialogue structure and inter-utterance dependency provides some increase in performance; learners that account for sequential dependencies (CRFs) show the best performance. We report our results from testing using a corpus of chat dialogues derived from online shopping customer-feedback data.
J.Allen and M.Core. Draft of DAMSL: Dialog Act Markup in Several Layers. The Multiparty Discourse Group. University of Rochester, Rochester, USA. 1997. A. Anderson, M. Bader, E. Bard, E. Boyle G.M. Doherty, S. Garrod, S. Isard, J. Kowtko, J. McAllister, J. Miller, C. Sotillo, H.S. Thompson, R. and Weinert. The HCRC Map Task Corpus. Language and Speech. 1991, 34, pp. 351–366. J. Ang, Y. Liu and E. Shriberg. Automatic Dialog Act Segmentation and Classification in Multiparty Meetings. IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005, pp, 1061–1064. S. Bangalore, G. Di Fabbrizio and A. Stent. Learning the Structure of Task-Driven Human-Human Dialogs. Proceedings of the 21st COLING and 44th ACL. 2006, pp. 201–208. H. H. Bui. A general model for online probabilistic plan recognition. IJCAI. 2003, pp. 1309–13 18. G.Y Chung. Sentence retrieval for abstracts of randomized controlled trials. BMC Medical Informatics and Decision Making. 2009, 9(10), pp. 1–13. F. Debole and F. Sebastiani. Supervised term weighting for automated text categorization. 18th ACM Symposium on Applied Computing. 2003, pp. 784–788. ClOpDpExNoQuRqAckStTaYesYN Op Cl Dp 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ex 0 0 0 0 0 0 No 0 0 0 0 0 0 Qu Rq Ack St 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 Ta 1 0 0 0 0 0 Yes 0 0 0 0 0 0 YN 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 YN010000000000 0 0 0 0 0 0 0 0 0 0 0 9: Confusion matrix for errors from the CRF with BoW+Position+Author+LabelAuthor (rows = correct classification; columns = misclassification; CONVENTIONALCLOSING = Cl; CONVENTIONALOPENING = Op; DOWNPLAYER = Dp; EXPRESSIVE = Ex; NOANSWER = No; OPENQUESTION = Qu; REQUEST = Rq; RESPONSEACK = Ack; STATEMENT = St; THANKS = Ta; YESANSWER = Yes; and YESNOQUESTION = YN) Table E. N. Forsyth. Improving Automated Lexical and Discourse Analysis of Online Chat Dialog. Master’s thesis. Naval Postgraduate School, 2007. J. Godfrey and E. Holliman and J. McDaniel. SWITCHBOARD: Telephone speech corpus for research and development. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 1992, pp. 517–520. S. Grau, E. Sanchis, M. Jose and D. Vilar. Dialogue act classification using a Bayesian approach. Proceedings of the 9th Conference on Speech and Computer. 2004. P. A. Heeman and J. Allen. The Trains 93 Dialogues. Trains Technical Note 94-2. Computer Science Dept., University of Rochester, March 1995. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. Proceedings of European Conference on Machine Learning. 1998, pp. 137–142. M. Johnston, S. Bangalore, G. Vasireddy, A. Stent, P. Ehlen, M. Walker, S. Whittaker and P. Maloor. MATCH: An Architecture for Multimodal Dialogue Systems. Proceedings of 40th ACL. 2002, pp. 376– 383. F. N. Julia and K. M. Iftekharuddin. Dialog Act classification using acoustic and discourse information of MapTask Data. Proceedings of the International Joint Conference on Neural Networks. 2008, pp. 1472– 1479. D. Jurafsky, E. Shriberg, B Fox and T. Curl. Lexical, Prosodic, and Syntactic Cues for Dialog Acts. Proceedings of ACL/COLING-98 Workshop on Discourse Relations and Discourse Markers. 1998, pp. 114–120. 870 E. Ivanovic. Automatic instant messaging dialogue using statistical models and dialogue acts. Master’s Thesis. The University of Melbourne. 2008. S. Keizer. A Bayesian Approach to Dialogue Act Classification. 5th Workshop on Formal Semantics and Pragmatics of Dialogue. 2001, pp. 210–218. S.N. Kim and L. Wang and T. Baldwin. Tagging and Linking Web Forum Posts. Fourteenth Conference on Computational Natural Language Learning. 2010. J. Lafferty, A. McCallum and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of ICML. 2001, pp. 282–289. D. J. Litman and S. Silliman. ITSPOKE: An Intelligent Tutoring Spoken Dialogue SYstem. Proceedings of the HLT/NAACL. 2004. M. M. Louwerse and S. Crossley. Dialog Act Classification Using N-Gram Algorithms. FLAIRS Conference, 2006, pp. 758–763. G. Minnen, J. Carroll and D. Pearce. Applied morphological processing of English Natural Language Engineering 2000, 7(3), pp. 77–80. M. Purver, J. Niekrasz and S. Peters. Ontology-Based Discourse Understanding for a Persistent Meeting As- sistant. 2005 Workshop on The Virtuality Continuum Revisited. 2005. K. Samuel, Sandra Carberry and K. Vijay-Shanker. Dialogue Act Tagging with Transformation-Based Learning. Proceedings of COLING/ACL 1998. 1998, pp. 1150-1 156. E. Shriberg, R. Bates, P. Taylor, A. Stolcke, D. Jurafsky, K. Ries, N. Coccaro, R. Martin, M. Meteer and C. Van Proc. CHI Ess-Dykema. Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?. Language and Speech. 1998, 41(3-4), pp. 439–487. V. R. Sridhar, S. Bangalore and S. Narayanan. Combining lexical, syntactic and prosodic cues for improved online dialog act tagging. Computer Speech and Language. 2009, 23(4), pp. 407–422. A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates, D. Jurafsky, P. Taylor, R. Martin, C. Van Ess-Dykema and M. Meteer. Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics. 2000, 26(3), pp. 339–373. A. Stolcke and E. Shriberg. Markovian Combination of Language and Prosodic Models for better Speech Understanding and Recognition . Invited talk at the IEEE Workshop on Speech Recognition and Understanding, Madonna di Campiglio, Italy, December 2001 2001, C. C. Werry. Linguistic and interactional features of Internet Relay Chat. In S. C. Herring (ed.). ComputerMediated Communication. Benjamins, 1996. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005. T. Wu, F. M. Khan, T. A. Fisher, L. A. Shuler and W. M. Pottenger. Posting act tagging using transformationbased learning. Proceedings of the Workshop on Foundations of Data Mining and Discovery, IEEE International Conference on Data Mining. 2002. 871