@inproceedings{luo-tang-2020-funny3,
title = "Funny3 at {S}em{E}val-2020 Task 7: Humor Detection of Edited Headlines with {LSTM} and {TFIDF} Neural Network System",
author = "Luo, Xuefeng and
Tang, Kuan",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.132",
doi = "10.18653/v1/2020.semeval-1.132",
pages = "1013--1018",
abstract = "This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 {``}Assessing the Funniness of Edited News Headlines{''}. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="luo-tang-2020-funny3">
<titleInfo>
<title>Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xuefeng</namePart>
<namePart type="family">Luo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kuan</namePart>
<namePart type="family">Tang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Committee for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona (online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 “Assessing the Funniness of Edited News Headlines”. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.</abstract>
<identifier type="citekey">luo-tang-2020-funny3</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.132</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.132</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1013</start>
<end>1018</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System
%A Luo, Xuefeng
%A Tang, Kuan
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F luo-tang-2020-funny3
%X This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 “Assessing the Funniness of Edited News Headlines”. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.
%R 10.18653/v1/2020.semeval-1.132
%U https://aclanthology.org/2020.semeval-1.132
%U https://doi.org/10.18653/v1/2020.semeval-1.132
%P 1013-1018
Markdown (Informal)
[Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System](https://aclanthology.org/2020.semeval-1.132) (Luo & Tang, SemEval 2020)
ACL