Abstract—For context-based recommendation systems, it is
important to determine intentions from indirect speech acts.
An algorithm of deriving intentions from indirect speech acts
has been proposed, but the algorithm included unclear portions
and there were no important experimental results for kinds of
speech acts. Therefore, this paper proposes an improved
algorithm and two experimental observations are discussed for
accuracies and kinds of answers in indirect speech acts.
Logical formulas are rewritten to if-else statements and the
number of conditions is reduced from 24 to 8 in the algorithm.
From experimental results, it is verified that the correct rate of
the proposed method is 48.2 points higher than the one of the
traditional method in indirect speech acts. Answers of "what"
most include indirect speech acts and the accuracy of the
proposed method is 53.8 points higher than the traditional one
in them.
Index Terms—Recommendation system, indirect speech acts,
affirmative intention, negative intention.
The authors are with University of Tokushima, Tokushima, Japan (email:
ogawa-takuki@iss.tokushima-u.ac.jp, kam@is.tokushima-u.ac.jp,
fuketa@is.tokushima-u.ac.jp, aoe@is.tokushima-u.ac.jp).
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Cite:Takuki Ogawa, Kazuhiro Morita, Masao Fuketa, and Jun-Ichi Aoe, "Effects of an Algorithm with a Recommendation Tree for Indirect Speech Acts," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 113-117, 2014.
