<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="elango-uppal-2018-riddl">
    <titleInfo>
        <title>RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Venkatesh</namePart>
        <namePart type="family">Elango</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Karan</namePart>
        <namePart type="family">Uppal</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>We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.</abstract>
    <identifier type="citekey">elango-uppal-2018-riddl</identifier>
    <identifier type="doi">10.18653/v1/S18-1054</identifier>
    <location>
        <url>https://aclanthology.org/S18-1054/</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>358</start>
            <end>363</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning
%A Elango, Venkatesh
%A Uppal, Karan
%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 elango-uppal-2018-riddl
%X We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.
%R 10.18653/v1/S18-1054
%U https://aclanthology.org/S18-1054/
%U https://doi.org/10.18653/v1/S18-1054
%P 358-363

Markdown (Informal)

[RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning](https://aclanthology.org/S18-1054/) (Elango & Uppal, SemEval 2018)

• RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning (Elango & Uppal, SemEval 2018)

ACL

• Venkatesh Elango and Karan Uppal. 2018. RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 358–363, New Orleans, Louisiana. Association for Computational Linguistics.