Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases

Kristian Nørgaard Jensen; Nicolaj Filrup Rasmussen; Thai Wang; Marco Placenti; Barbara Plank

Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases

Kristian Nørgaard Jensen, Nicolaj Filrup Rasmussen, Thai Wang, Marco Placenti, Barbara Plank

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

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.

Original language	English
Title of host publication	SemEval
Publisher	Association for Computational Linguistics
Publication date	2020
Publication status	Published - 2020

Keywords

humour intensity assessment
edited news headlines
SemEval-2020
regression model
automatic humour detection

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Final-SemEval2020-Task7Accepted author manuscript, 257 KBLicence: CC BY

Cite this

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title = "Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases",

abstract = "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.",

keywords = "humour intensity assessment, edited news headlines, SemEval-2020, regression model, automatic humour detection",

author = "Jensen, {Kristian N{\o}rgaard} and {Filrup Rasmussen}, Nicolaj and Thai Wang and Marco Placenti and Barbara Plank",

year = "2020",

language = "English",

booktitle = "SemEval",

publisher = "Association for Computational Linguistics",

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Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases. / Jensen, Kristian Nørgaard; Filrup Rasmussen, Nicolaj; Wang, Thai et al.
SemEval. Association for Computational Linguistics, 2020.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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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

M3 - Article in proceedings

BT - SemEval

PB - Association for Computational Linguistics

ER -

Buhscitu at SemEvaL-2020 Task 7: Assessing Humour in Edited News Headlines using Hand-Crafted Features and Online Knowledge Bases

Abstract

Keywords

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