EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption

Liyuan Zhou; Qiongkai Xu; Hanna Suominen; Tom Gedeon

doi:10.18653/v1/S18-1071

EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption

Liyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.

Anthology ID:: S18-1071
Volume:: Proceedings of the 12th International Workshop on Semantic Evaluation
Month:: June
Year:: 2018
Address:: New Orleans, Louisiana
Editors:: Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 449–453
Language:
URL:: https://aclanthology.org/S18-1071/
DOI:: 10.18653/v1/S18-1071
Bibkey:
Cite (ACL):: Liyuan Zhou, Qiongkai Xu, Hanna Suominen, and Tom Gedeon. 2018. EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 449–453, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):: EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption (Zhou et al., SemEval 2018)
Copy Citation:
PDF:: https://aclanthology.org/S18-1071.pdf
Note:: S18-1071.Notes.pdf

PDF Cite Search Note Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{zhou-etal-2018-epution,
    title = "{EPUTION} at {S}em{E}val-2018 Task 2: Emoji Prediction with User Adaption",
    author = "Zhou, Liyuan  and
      Xu, Qiongkai  and
      Suominen, Hanna  and
      Gedeon, Tom",
    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/S18-1071/",
    doi = "10.18653/v1/S18-1071",
    pages = "449--453",
    abstract = "This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {---} more precisely, Twitter {---} with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users' tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2018-epution">
    <titleInfo>
        <title>EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Liyuan</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qiongkai</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hanna</namePart>
        <namePart type="family">Suominen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tom</namePart>
        <namePart type="family">Gedeon</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 our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.</abstract>
    <identifier type="citekey">zhou-etal-2018-epution</identifier>
    <identifier type="doi">10.18653/v1/S18-1071</identifier>
    <location>
        <url>https://aclanthology.org/S18-1071/</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>449</start>
            <end>453</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption
%A Zhou, Liyuan
%A Xu, Qiongkai
%A Suominen, Hanna
%A Gedeon, Tom
%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 zhou-etal-2018-epution
%X This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.
%R 10.18653/v1/S18-1071
%U https://aclanthology.org/S18-1071/
%U https://doi.org/10.18653/v1/S18-1071
%P 449-453

Download as File

Markdown (Informal)

[EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption](https://aclanthology.org/S18-1071/) (Zhou et al., SemEval 2018)

EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption (Zhou et al., SemEval 2018)

ACL

Liyuan Zhou, Qiongkai Xu, Hanna Suominen, and Tom Gedeon. 2018. EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 449–453, New Orleans, Louisiana. Association for Computational Linguistics.