A text-driven rule-based system for emotion cause detection

  • Authors:

  • Sophia Yat Mei Lee;Ying Chen;Chu-Ren Huang

  • Affiliations:

  • The Hong Kong Polytechnic University;China Agriculture University and The Hong Kong Polytechnic University;Institute of Linguistics Academia Sinica and The Hong Kong Polytechnic University

  • Venue:
  • CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
  • Year:
  • 2010

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Abstract

Emotion cause detection is a new research area in emotion processing even though most theories of emotion treat recognition of a triggering cause event as an integral part of emotion. As a first step towards fully automatic inference of cause-emotion correlation, we propose a text-driven, rule-based approach to emotion cause detection in this paper. First of all, a Chinese emotion cause annotated corpus is constructed based on our proposed annotation scheme. By analyzing the corpus data, we identify seven groups of linguistic cues and generalize two sets of linguistic rules for detection of emotion causes. With the linguistic rules, we then develop a rule-based system for emotion cause detection. In addition, we propose an evaluation scheme with two phases for performance assessment. Experiments show that our system achieves a promising performance for cause occurrence detection as well as cause event detection. The current study should lay the ground for future research on the inferences of implicit information and the discovery of new information based on cause-event relation.