<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shah-etal-2018-neural">
    <titleInfo>
        <title>Neural Network based Extreme Classification and Similarity Models for Product Matching</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kashif</namePart>
        <namePart type="family">Shah</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Selcuk</namePart>
        <namePart type="family">Kopru</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jean-David</namePart>
        <namePart type="family">Ruvini</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 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Srinivas</namePart>
            <namePart type="family">Bangalore</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jennifer</namePart>
            <namePart type="family">Chu-Carroll</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yunyao</namePart>
            <namePart type="family">Li</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>Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.</abstract>
    <identifier type="citekey">shah-etal-2018-neural</identifier>
    <identifier type="doi">10.18653/v1/N18-3002</identifier>
    <location>
        <url>https://aclanthology.org/N18-3002</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>8</start>
            <end>15</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Network based Extreme Classification and Similarity Models for Product Matching
%A Shah, Kashif
%A Kopru, Selcuk
%A Ruvini, Jean-David
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F shah-etal-2018-neural
%X Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.
%R 10.18653/v1/N18-3002
%U https://aclanthology.org/N18-3002
%U https://doi.org/10.18653/v1/N18-3002
%P 8-15

Markdown (Informal)

[Neural Network based Extreme Classification and Similarity Models for Product Matching](https://aclanthology.org/N18-3002) (Shah et al., NAACL 2018)

• Neural Network based Extreme Classification and Similarity Models for Product Matching (Shah et al., NAACL 2018)

ACL

• Kashif Shah, Selcuk Kopru, and Jean-David Ruvini. 2018. Neural Network based Extreme Classification and Similarity Models for Product Matching. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 8–15, New Orleans - Louisiana. Association for Computational Linguistics.