@inproceedings{garibo-i-orts-2019-multilingual,
title = "Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter at {S}em{E}val-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection",
author = "Garibo i Orts, {\`O}scar",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2081/",
doi = "10.18653/v1/S19-2081",
pages = "460--463",
abstract = "This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . The task is divided in two sub-tasks. Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not. Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text. In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment. Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="garibo-i-orts-2019-multilingual">
<titleInfo>
<title>Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Òscar</namePart>
<namePart type="family">Garibo i Orts</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 13th International Workshop on Semantic Evaluation</title>
</titleInfo>
<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>
<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">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saif</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Mohammad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Minneapolis, Minnesota, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . The task is divided in two sub-tasks. Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not. Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text. In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment. Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases.</abstract>
<identifier type="citekey">garibo-i-orts-2019-multilingual</identifier>
<identifier type="doi">10.18653/v1/S19-2081</identifier>
<location>
<url>https://aclanthology.org/S19-2081/</url>
</location>
<part>
<date>2019-06</date>
<extent unit="page">
<start>460</start>
<end>463</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection
%A Garibo i Orts, Òscar
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F garibo-i-orts-2019-multilingual
%X This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . The task is divided in two sub-tasks. Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not. Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text. In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment. Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases.
%R 10.18653/v1/S19-2081
%U https://aclanthology.org/S19-2081/
%U https://doi.org/10.18653/v1/S19-2081
%P 460-463
Markdown (Informal)
[Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection](https://aclanthology.org/S19-2081/) (Garibo i Orts, SemEval 2019)
ACL