TY - GEN
T1 - Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases
AU - Jensen, Kristian Nørgaard
AU - Filrup Rasmussen, Nicolaj
AU - Wang, Thai
AU - Placenti, Marco
AU - Plank, Barbara
PY - 2020
Y1 - 2020
N2 - This paper describes our system to assess humour intensity in edited news headlines as part of a participation in the 7th task of SemEval-2020 on “Humor, Emphasis and Sentiment”. Various factors need to be accounted for in order to assess the funniness of an edited headline. We propose an architecture that uses hand-crafted features, knowledge bases and a language model to understand humour, and combines them in a regression model. Our system outperforms two baselines. In general, automatic humour assessment remains a difficult task.
AB - This paper describes our system to assess humour intensity in edited news headlines as part of a participation in the 7th task of SemEval-2020 on “Humor, Emphasis and Sentiment”. Various factors need to be accounted for in order to assess the funniness of an edited headline. We propose an architecture that uses hand-crafted features, knowledge bases and a language model to understand humour, and combines them in a regression model. Our system outperforms two baselines. In general, automatic humour assessment remains a difficult task.
KW - humour intensity assessment
KW - edited news headlines
KW - SemEval-2020
KW - regression model
KW - automatic humour detection
KW - humour intensity assessment
KW - edited news headlines
KW - SemEval-2020
KW - regression model
KW - automatic humour detection
M3 - Article in proceedings
BT - SemEval
PB - Association for Computational Linguistics
ER -