Stepwise Noise Elimination for Better Motivational and Advisory Texts Classification
Patrycja Swieczkowska, Rafal Rzepka, and Kenji Araki
Language Media Laboratory, Division of Media and Network Technologies, Graduate School of Information Science and Technology, Hokkaido University
Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido 060-0814, Japan
There is little research into designing artificial motivational agents. The end-goal of our studies is therefore to create a dialogue system that would motivate users to do their everyday tasks using natural language. In this paper, we present a method of distinguishing texts containing motivational advice from regular texts to sort out noise in training data for our dialogue system. We implemented a novel method of chaining two shallow networks together by utilizing the output results of the first network to determine the input for the second one. We achieved F-score of 0.94 and 0.97 with our proposed method. The contributions of this paper are threefold: first, we successfully identified 14 hand-crafted features that make a text motivational/advisory. Secondly, we were able to create a classifying algorithm that distinguishes motivational/advisory texts from regular ones. Finally, our proposed method can be applied to other text classification tasks.
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