@inproceedings{hussein-etal-2020-nlp,
title = "{NLP}{\_}{P}assau at {S}em{E}val-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in {E}nglish, {D}anish and {T}urkish",
author = "Hussein, Omar and
Sfar, Hachem and
Mitrovi{\'c}, Jelena and
Granitzer, Michael",
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-1.277",
doi = "10.18653/v1/2020.semeval-1.277",
pages = "2090--2097",
abstract = "This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to identify offensive speech in social media, particularly in tweets. The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN). A multidimensional numerical representation (embedding) for each of the words in the tweets that were used by the model were determined using fastText. This allowed for using a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning. This model was used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88{\%}, 76.76{\%} and 76.70{\%}, respectively.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hussein-etal-2020-nlp">
<titleInfo>
<title>NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish</title>
</titleInfo>
<name type="personal">
<namePart type="given">Omar</namePart>
<namePart type="family">Hussein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hachem</namePart>
<namePart type="family">Sfar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jelena</namePart>
<namePart type="family">Mitrović</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Granitzer</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 describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to identify offensive speech in social media, particularly in tweets. The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN). A multidimensional numerical representation (embedding) for each of the words in the tweets that were used by the model were determined using fastText. This allowed for using a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning. This model was used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88%, 76.76% and 76.70%, respectively.</abstract>
<identifier type="citekey">hussein-etal-2020-nlp</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.277</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.277</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>2090</start>
<end>2097</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish
%A Hussein, Omar
%A Sfar, Hachem
%A Mitrović, Jelena
%A Granitzer, Michael
%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 hussein-etal-2020-nlp
%X This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to identify offensive speech in social media, particularly in tweets. The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN). A multidimensional numerical representation (embedding) for each of the words in the tweets that were used by the model were determined using fastText. This allowed for using a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning. This model was used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88%, 76.76% and 76.70%, respectively.
%R 10.18653/v1/2020.semeval-1.277
%U https://aclanthology.org/2020.semeval-1.277
%U https://doi.org/10.18653/v1/2020.semeval-1.277
%P 2090-2097
Markdown (Informal)
[NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish](https://aclanthology.org/2020.semeval-1.277) (Hussein et al., SemEval 2020)
ACL