An Estimation of Favorite Value in Emotion Generating Calculation by Fuzzy Petri Net

TL;DR

This paper proposes a fuzzy Petri net-based learning method to estimate personal taste values in emotion calculation models, enhancing their ability to handle unknown preferences from dialogue data.

Contribution

It introduces a novel learning approach using Fuzzy Petri Nets to estimate Favorite Values in emotion generating calculations, addressing the issue of missing personal taste data.

Findings

Effective FV estimation from dialogue improves emotion recognition accuracy.

Fuzzy Petri Net-based method successfully handles unknown preference values.

Enhanced EGC model better captures individual emotional responses.

Abstract

Emotion Generating Calculations (EGC) method based on the Emotion Eliciting Condition Theory can decide whether an event arouses pleasure or not and quantify the degree under the event. An event in the form of Case Frame representation is classified into 12 types of calculations. However, the weak point in EGC is Favorite Value (FV) as the personal taste information. In order to improve the problem, this paper challenges to establish a learning method to learn speaker's taste information from dialog. Especially, the learning method employs Fuzzy Petri Net to find an appropriate FV to a word which has the unknown FV. This paper discusses the effective learning method to improve a weak point of EGC when a missing value of FV exists.