This paper analyzes the performance of a domain-general sarcasm detection system on datasets from two different domains: Twitter and Amazon product reviews.
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This paper analyzes the performance of a domain-general sarcasm detection system on datasets from two different domains: Twitter and Amazon product reviews.
Physical Description
6 p.
Notes
Abstract: Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text. We analyze the performance of a domain-general sarcasm detection system on datasets from two very different domains: Twitter, and Amazon product reviews. We categorize the errors that we identify with each, and make recommendations for addressing these issues in NLP systems in the future.
Publication Title:
Proceedings of the Workshop on Computational Semantics beyond Events and Roles (SemBEaR-2018)
Pages:
6
Page Start:
21
Page End:
26
Peer Reviewed:
Yes
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