<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chakraborty-etal-2019-sparse">
    <titleInfo>
        <title>Sparse Victory – A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rupak</namePart>
        <namePart type="family">Chakraborty</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ashima</namePart>
        <namePart type="family">Elhence</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kapil</namePart>
        <namePart type="family">Arora</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets. Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair. We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e. precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels). Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5% (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.</abstract>
    <identifier type="citekey">chakraborty-etal-2019-sparse</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_022</identifier>
    <location>
        <url>https://aclanthology.org/R19-1022</url>
    </location>
    <part>
        <date>2019-09</date>
        <extent unit="page">
            <start>188</start>
            <end>197</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Sparse Victory – A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification
%A Chakraborty, Rupak
%A Elhence, Ashima
%A Arora, Kapil
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F chakraborty-etal-2019-sparse
%X In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets. Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair. We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e. precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels). Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5% (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.
%R 10.26615/978-954-452-056-4_022
%U https://aclanthology.org/R19-1022
%U https://doi.org/10.26615/978-954-452-056-4_022
%P 188-197

Markdown (Informal)

[Sparse Victory – A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification](https://aclanthology.org/R19-1022) (Chakraborty et al., RANLP 2019)

• Sparse Victory – A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification (Chakraborty et al., RANLP 2019)

ACL

• Rupak Chakraborty, Ashima Elhence, and Kapil Arora. 2019. Sparse Victory – A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 188–197, Varna, Bulgaria. INCOMA Ltd..