Unsupervised Irony Detection: A Probabilistic Model with Word Embeddings

@inproceedings{Nozza2016UnsupervisedID,
  title={Unsupervised Irony Detection: A Probabilistic Model with Word Embeddings},
  author={Debora Nozza and Elisabetta Fersini and Enza Messina},
  booktitle={KDIR},
  year={2016}
}
The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). This is because machine learning methods can be easily misled by the presence of words that have a strong polarity but are used ironically, which means that the opposite polarity was intended. In this paper, we propose an unsupervised framework for domain-independent irony detection. In particular, to derive an unsupervised Topic-Irony… CONTINUE READING

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