<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dhyani-2018-ohiostate">
    <titleInfo>
        <title>OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dushyanta</namePart>
        <namePart type="family">Dhyani</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-jun</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of The 12th International Workshop on Semantic Evaluation</title>
        </titleInfo>
        <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 describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation significantly boosts the performance of the model. Our best-performing systems stood 8th out of 20 teams on the classification task on noisy data and 12th out of 28 teams on the classification task on clean data.</abstract>
    <identifier type="citekey">dhyani-2018-ohiostate</identifier>
    <identifier type="doi">10.18653/v1/S18-1124</identifier>
    <location>
        <url>https://aclanthology.org/S18-1124</url>
    </location>
    <part>
        <date>2018-jun</date>
        <extent unit="page">
            <start>783</start>
            <end>787</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks
%A Dhyani, Dushyanta
%S Proceedings of The 12th International Workshop on Semantic Evaluation
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F dhyani-2018-ohiostate
%X We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation significantly boosts the performance of the model. Our best-performing systems stood 8th out of 20 teams on the classification task on noisy data and 12th out of 28 teams on the classification task on clean data.
%R 10.18653/v1/S18-1124
%U https://aclanthology.org/S18-1124
%U https://doi.org/10.18653/v1/S18-1124
%P 783-787

Markdown (Informal)

[OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks](https://aclanthology.org/S18-1124) (Dhyani, SemEval 2018)

• OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks (Dhyani, SemEval 2018)

ACL

• Dushyanta Dhyani. 2018. OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 783–787, New Orleans, Louisiana. Association for Computational Linguistics.