<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pecar-etal-2019-improving">
    <titleInfo>
        <title>Improving Sentiment Classification in Slovak Language</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Samuel</namePart>
        <namePart type="family">Pecar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marian</namePart>
        <namePart type="family">Simko</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Bielikova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tomaž</namePart>
            <namePart type="family">Erjavec</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michał</namePart>
            <namePart type="family">Marcińczuk</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jakub</namePart>
            <namePart type="family">Piskorski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lidia</namePart>
            <namePart type="family">Pivovarova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Šnajder</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Josef</namePart>
            <namePart type="family">Steinberger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Yangarber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.</abstract>
    <identifier type="citekey">pecar-etal-2019-improving</identifier>
    <identifier type="doi">10.18653/v1/W19-3716</identifier>
    <location>
        <url>https://aclanthology.org/W19-3716</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>114</start>
            <end>119</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Sentiment Classification in Slovak Language
%A Pecar, Samuel
%A Simko, Marian
%A Bielikova, Maria
%Y Erjavec, Tomaž
%Y Marcińczuk, Michał
%Y Nakov, Preslav
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F pecar-etal-2019-improving
%X Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.
%R 10.18653/v1/W19-3716
%U https://aclanthology.org/W19-3716
%U https://doi.org/10.18653/v1/W19-3716
%P 114-119

Markdown (Informal)

[Improving Sentiment Classification in Slovak Language](https://aclanthology.org/W19-3716) (Pecar et al., BSNLP 2019)

• Improving Sentiment Classification in Slovak Language (Pecar et al., BSNLP 2019)

ACL

• Samuel Pecar, Marian Simko, and Maria Bielikova. 2019. Improving Sentiment Classification in Slovak Language. In Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing, pages 114–119, Florence, Italy. Association for Computational Linguistics.