@inproceedings{durlich-2018-kluenicorn,
title = "{KLUE}nicorn at {S}em{E}val-2018 Task 3: A Naive Approach to Irony Detection",
author = {D{\"u}rlich, Luise},
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1099",
doi = "10.18653/v1/S18-1099",
pages = "607--612",
abstract = "This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on {``}Irony detection in English tweets{''}. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="durlich-2018-kluenicorn">
<titleInfo>
<title>KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luise</namePart>
<namePart type="family">Dürlich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 12th International Workshop on Semantic Evaluation</title>
</titleInfo>
<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>
<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">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marine</namePart>
<namePart type="family">Carpuat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">New Orleans, Louisiana</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on “Irony detection in English tweets”. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.</abstract>
<identifier type="citekey">durlich-2018-kluenicorn</identifier>
<identifier type="doi">10.18653/v1/S18-1099</identifier>
<location>
<url>https://aclanthology.org/S18-1099</url>
</location>
<part>
<date>2018-06</date>
<extent unit="page">
<start>607</start>
<end>612</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection
%A Dürlich, Luise
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F durlich-2018-kluenicorn
%X This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on “Irony detection in English tweets”. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.
%R 10.18653/v1/S18-1099
%U https://aclanthology.org/S18-1099
%U https://doi.org/10.18653/v1/S18-1099
%P 607-612
Markdown (Informal)
[KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection](https://aclanthology.org/S18-1099) (Dürlich, SemEval 2018)
ACL