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JACIII Vol.16 No.1 pp. 62-68
doi: 10.20965/jaciii.2012.p0062
(2012)

Paper:

A Study on Conversational Content Recognition Method Using JapaneseWordNet for Robot-Assisted Therapies

Yuta Izutsu*1, Hiroharu Kawanaka*1, Koji Yamamoto*2, Kiyoshi Suzuki*3, Haruhiko Takase*1,
and Shinji Tsuruoka*4

*1Graduate School of Engineering, Mie University, Tsu, Mie 514-8507, Japan

*2Faculty of Medical Engineering, Suzuka University of Medical Science, Suzuka, Mie 510-0293, Japan

*3Social Welfare Corporation Taiyo No Sato, Matsusaka, Mie 515-0818, Japan

*4Graduate School of Regional Innovation Studies, Mie University, Tsu, Mie 514-8507, Japan

Received:
July 4, 2011
Accepted:
October 12, 2011
Published:
January 20, 2012
Keywords:
robot-assisted therapy, conversation content recognition, JapaneseWordNet, concept dictionary
Abstract

This paper proposes a conversational content recognition method for robot-assisted therapies. In the proposed method, nouns in the conversation are first extracted by morphological analysis techniques. As the next step, the dominant conception of them, which is called “synset” in this paper, are obtained by using a concept dictionary. And finally the conversational topic is determined considering the number and kinds of obtained synsets. In this paper, Japanese Word-Net was employed as concept dictionary, and evaluation experiments using daily conversation voice data recorded in the welfare facility were conducted. The obtained results by the proposed method were quite similar to those by human and indicated that the proposed method had enough possibility to recognize conversation among some persons. This paper describes the detail of the proposed method, experimental results and also does some problems about the proposed method.

Cite this article as:
Y. Izutsu, H. Kawanaka, K. Yamamoto, K. Suzuki, H. Takase, and <. Tsuruoka, “A Study on Conversational Content Recognition Method Using JapaneseWordNet for Robot-Assisted Therapies,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.1, pp. 62-68, 2012.
Data files:
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Last updated on Oct. 18, 2019