Event-Driven Emotion Cause Extraction with Corpus Construction

@inproceedings{Gui2016EventDrivenEC,
  title={Event-Driven Emotion Cause Extraction with Corpus Construction},
  author={Lin Gui and Dongyin Wu and Ruifeng Xu and Qin Lu and Yu Zhou},
  booktitle={EMNLP},
  year={2016}
}
In this paper, we present our work in emotion cause extraction. [...] Key Method Thus, we first present a dataset we built using SINA city news. The annotation is based on the scheme of the W3C Emotion Markup Language. Second, we propose a 7-tuple definition to describe emotion cause events. Based on this general definition, we propose a new event-driven emotion cause extraction method using multi-kernel SVMs where a syntactical tree based approach is used to represent events in text.Expand
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References

SHOWING 1-10 OF 35 REFERENCES
A Text-driven Rule-based System for Emotion Cause Detection
TLDR
A text-driven, rule-based approach to emotion cause detection is proposed and a promising system achieves a promising performance for cause occurrence detection as well as cause event detection.
Emotion Cause Detection with Linguistic Construction in Chinese Weibo Text
TLDR
A rule- based emotion cause detection method is developed which uses 25 manually complied rules and two machine learning based cause detection methods are developed including a classification-based method using support vec- tor machines and a sequence labeling based method using conditional random fields model.
Text-based emotion classification using emotion cause extraction
TLDR
A novel method for identifying emotions in microblog posts based on the theory that a triggering cause event is an integral part of emotion, and an automatic rule-based system to detect and extract the cause event of each emotional post is constructed.
Emotion Cause Detection with Linguistic Constructions
TLDR
The multi-label model not only detects multi-clause causes, but also captures the long-distance information to facilitate emotion cause detection and creates two sets of linguistic patterns during feature extraction.
A rule-based approach to emotion cause detection for Chinese micro-blogs
TLDR
A rule-based system underlying the conditions that trigger emotions based on an emotional model, based on Bayesian probability is proposed and the experimental results validate the feasibility of the approach.
Detecting Emotion Stimuli in Emotion-Bearing Sentences
TLDR
A CRF learner is built, a sequential learning model to detect the emotion stimulus spans in emotion-bearing sentences and it is shown that the model significantly outperforms all the baselines.
Coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context
This paper proposes a novel approach using a coarse-to-fine analysis strategy for sentence-level emotion classification which takes into consideration of similarities to sentences in training set as
EMOCause: an easy-adaptable approach to emotion cause contexts
In this paper we present a method to automatically identify linguistic contexts which contain possible causes of emotions or emotional states from Italian newspaper articles (La Repubblica Corpus).
A New Emotion Dictionary based on the Distinguish of Emotion Expression and Emotion Cognition
TLDR
A text emotion computing framework based on "cognitive stimulation-reflective expression" mechanism is proposed and a new type of emotion dictionary is constructed with a clear framework, rich emotional knowledge and low ambiguity.
JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis
TLDR
A probabilistic model named JEAM is developed and an EM algorithm is introduced to solve the likelihood maximum problem and to obtain the latent emotion distribution of the author and a supervised learning method is utilized to assign the sentiment polarity to a given online review.
...
1
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3
4
...