The Determination of Affirmative and Negative Intentions for Indirect Speech Acts by a Recommendation Tree

For context-based recommendation systems, it is necessary to detect affirmative and negative intentions from answers. However, traditional studies cannot determine these intentions from indirect speech acts. In order to determine these intentions from indirect speech acts, this paper defines a recommendation tree and proposes an algorithm of deriving intentions of indirect speech acts by the tree. In the proposed method, a recommendation condition (RC) is introduced and it is classified into a required RC, a selectable RC, and a not-selectable RC. The recommendation tree is constructed by nodes and edges corresponding to these three conditions. The deriving algorithm determines affirmative and negative intentions of indirect speech acts by tracing the trees. From experimental results, it is verified that the accuracy of the proposed method is about 40 points higher than the traditional method.