A Method for Using Discounted Utterances  in Spontaneous Conversation

Hiroki Yamaguchi; Yukio Ohsawa; Yoko Nishihara

doi:10.20965/jaciii.2010.p0825

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JACIII Vol.14 No.7 pp. 825-830

doi: 10.20965/jaciii.2010.p0825

(2010)

Paper:

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A Method for Using Discounted Utterances in Spontaneous Conversation

Hiroki Yamaguchi, Yukio Ohsawa, and Yoko Nishihara

Dept. of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan

Received:

April 1, 2010

Accepted:

June 22, 2010

Published:

November 20, 2010

Keywords:

discounted utterance, discourse analysis, spontaneous conversation

Abstract

Using Discounted Utterances (DUs) in spontaneous conversation by applying text mining technology, extraction, and evaluation, we focused on DUs where values were buried in previous conversations. We discovered DU potentials by reconsidering them through human-computer interaction. Onlinechat experiments clarified DU features and demonstrated our system’s importance. We found DUs involving (1) experiences shared by the subjects, (2) subjects’ unique experiences, concerns, or beliefs, and (3) apparent unimportance or unrecognized potential. Results of the experiments showed our evaluation method to be appropriate for calculating DU importance when DUs involving (3) were valued significantly lower than (1) and (2). Experiments also suggested that most DUs extracted by the system were not indeed completely ignored but included subjects’ unique stories involving main contexts. Such stories were based on subjects’ unique experiences and may be useful for helping subjects’ metacognition. The system may also enable nonsubjects to infer subjects and their thinking.

Cite this article as:

H. Yamaguchi, Y. Ohsawa, and Y. Nishihara, “A Method for Using Discounted Utterances in Spontaneous Conversation,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.7, pp. 825-830, 2010.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] G. Salton and M. J. McGill, “Introduction to Modern Information Retrieval,” McGraw-Hill, 1983.

[2] [2] M. Suwa, “A Cognitive Model of Acquiring Embodied Expertise Through Meta-cognitive Verbalization,” Information and Medea Technologies, Vol.3, No.2, pp. 399-408, 2008.

[3] [3] G. Salton, A. Wong, and C. S. Yang, “A Vector Space Model for Automatic Indexing,” Communication of the ACM, Vol.18, No.11, pp. 613-620, 1975.

[4] [4] R. Barzilay and M. Elhadad, “Using lexical chains for text summarization,” Advances in Automatic Text Summarization, pp. 1-12, The MIT Press, London, 1999.

[5] [5] C. Hori and S. Furui, “Summarization: An Approach through Word Extraction and a Method for Evaluation,” IEICE Trans. INF. & SYST., Vol.E87-D, No.1, pp. 15-25, 2004.

[6] [6] K. M. Hammouda and M. S. Kamel, “Efficient Phrase-Based Document Indexing for Web Document Clustering,” IEEE Trans. on Knowledge and Data Engineering, Vol.16, Issue 10, pp. 1279-1296, 2004.

[7] [7] A. Leuski, “Evaluating Document Clustering for Interactive Information Retrieval,” Proc. 2001 ACM Int. Conf. on Information and Knowledge Management, pp. 33-40. Atlanta, Georgia, USA, Nov. 2001.

[8] [8] K. Nishimoto, Y. Sumi, and K. Mase, “Enhancement of Creative Aspects of a Daily Conversation with a Topic Development Agent,” Coordination of Technology for Creative Applications, Vol.1364, pp. 63-76, Springer Berlin/Heidelberg, 1998.

[9] [9] N. Matsumura, Y. Ohsawa, and M. Ishizuka, “Automatic Indexing Based on Term Activity,” J. of Japanese Society for Artificial Intelligence, Vol.17, No.4 F, pp. 398-406, 2002.

[10] [10] M. Hearst, “TextTilling: Segmenting text into multi-paragraph subtopic passages,” Computational Linguistics, Vol.23, No.1, pp. 33-64, 1997.

[11] [11] Y. Ohsawa, “Chance Discoveries for Making Decisions in Complex Real World,” New Generation Computing, Vol.20, pp. 143-163, Ohmsha, Ltd. and Springer-Verlag, 2002.