<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="al-omari-etal-2019-emodet">
    <titleInfo>
        <title>EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hani</namePart>
        <namePart type="family">Al-Omari</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Malak</namePart>
        <namePart type="family">Abdullah</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nabeel</namePart>
        <namePart type="family">Bassam</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>Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).</abstract>
    <identifier type="citekey">al-omari-etal-2019-emodet</identifier>
    <identifier type="doi">10.18653/v1/S19-2032</identifier>
    <location>
        <url>https://aclanthology.org/S19-2032</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>200</start>
            <end>204</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning
%A Al-Omari, Hani
%A Abdullah, Malak
%A Bassam, Nabeel
%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 al-omari-etal-2019-emodet
%X Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).
%R 10.18653/v1/S19-2032
%U https://aclanthology.org/S19-2032
%U https://doi.org/10.18653/v1/S19-2032
%P 200-204

Markdown (Informal)

[EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning](https://aclanthology.org/S19-2032) (Al-Omari et al., SemEval 2019)

• EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning (Al-Omari et al., SemEval 2019)

ACL

• Hani Al-Omari, Malak Abdullah, and Nabeel Bassam. 2019. EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 200–204, Minneapolis, Minnesota, USA. Association for Computational Linguistics.