Summary of the paper
| Title | Emotion Cause Events: Corpus Construction and Analysis |
|---|---|
| Authors | Sophia Yat Mei Lee, Ying Chen, Shoushan Li and Chu-Ren Huang |
| Abstract | Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification. |
| Topics | Corpus (creation, annotation, etc.), Emotion Recognition/Generation, Text mining |
Full paper |
Emotion Cause Events: Corpus Construction and Analysis |
Slides |
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| Bibtex | @InProceedings{LEE10.322, author = {Sophia Yat Mei Lee and Ying Chen and Shoushan Li and Chu-Ren Huang}, title = {Emotion Cause Events: Corpus Construction and Analysis}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |