ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets

lib:af16828a0435d9f0 (v1.0.0)

Authors: Kevin Swanberg,Madiha Mirza,Ted Pedersen,Zhenduo Wang
Where published: SEMEVAL 2018 6
Document:  PDF  DOI 
Abstract URL: https://www.aclweb.org/anthology/S18-1082/


This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!