Comparing BERT Against Traditional Machine Learning Models in Text Classification | Journal of Computational and Cognitive Engineering

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Comparing BERT Against Traditional Machine Learning Models in Text Classification | Journal of Computational and Cognitive Engineering </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/4/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Computational and Cognitive Engineering"/> <meta name="citation_journal_abbrev" content="JCCE"/> <meta name="citation_issn" content="2810-9503"/> <meta name="citation_author" content="Eduardo C. 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It is able to cope with NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any corpus delivering great results has make this approach very popular in academia and industry. Although, other approaches have been used before successfully. We first present BERT and a review on classical NLP approaches. Then, we empirically test with a suite of different scenarios the behaviour of BERT against traditional TF-IDF vocabulary fed to machine learning models. The purpose of this work is adding empirical evidence to support the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique in NLP problems. 聽 Received: 10 March 2023 | Revised: 4 April 2023 | Accepted: 20 April 2023 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="citation_keywords" xml:lang="en" content="BERT"/> <meta name="citation_keywords" xml:lang="en" content="natural language processing"/> <meta name="citation_keywords" xml:lang="en" content="machine learning"/> <meta name="citation_keywords" xml:lang="en" content="comparison"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/download/838/394"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Eduardo C. Garrido-Merchan"/> <meta name="DC.Creator.PersonalName" content="Roberto Gozalo-Brizuela"/> <meta name="DC.Creator.PersonalName" content="Santiago Gonzalez-Carvajal"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-04-21"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-03-09"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-11-15"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-01-10"/> <meta name="DC.Description" xml:lang="en" content="The BERT model has arisen as a popular state-of-the-art model in recent years. It is able to cope with NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any corpus delivering great results has make this approach very popular in academia and industry. Although, other approaches have been used before successfully. We first present BERT and a review on classical NLP approaches. Then, we empirically test with a suite of different scenarios the behaviour of BERT against traditional TF-IDF vocabulary fed to machine learning models. The purpose of this work is adding empirical evidence to support the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique in NLP problems. 聽 Received: 10 March 2023 | Revised: 4 April 2023 | Accepted: 20 April 2023 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="838"/> <meta name="DC.Identifier.pageNumber" content="352-356"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJCCE3202838"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/838"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Computational and Cognitive 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class="pkp_structure_content has_sidebar"> <div class="pkp_structure_main" role="main"> <a id="pkp_content_main"></a> <div class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/JCCE/issue/view/67"> Vol. 2 No. 4 (2023) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research Articles </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Comparing BERT Against Traditional Machine Learning Models in Text Classification </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Eduardo C. Garrido-Merchan </span> <span class="affiliation"> Quantitative Methods Department, Comillas Pontifical University, Spain </span> </li> <li> <span class="name"> Roberto Gozalo-Brizuela </span> <span class="affiliation"> Quantitative Methods Department, Comillas Pontifical University, Spain </span> </li> <li> <span class="name"> Santiago Gonzalez-Carvajal </span> <span class="affiliation"> Artificial Intelligence Department, Polytechnic University of Madrid, Spain </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJCCE3202838"> https://doi.org/10.47852/bonviewJCCE3202838 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> BERT, natural language processing, machine learning, comparison </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>The BERT model has arisen as a popular state-of-the-art model in recent years. It is able to cope with NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any corpus delivering great results has make this approach very popular in academia and industry. Although, other approaches have been used before successfully. We first present BERT and a review on classical NLP approaches. Then, we empirically test with a suite of different scenarios the behaviour of BERT against traditional TF-IDF vocabulary fed to machine learning models. The purpose of this work is adding empirical evidence to support the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique in NLP problems.</p> <p>聽</p> <p><strong>Received</strong>: 10 March 2023 | <strong>Revised</strong>: 4 April 2023 | <strong>Accepted</strong>: 20 April 2023</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89267", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":1827,\"events_count_by_day\":[{\"count\":\"1\",\"date\":\"2023-04-21\"},{\"count\":\"1\",\"date\":\"2023-04-22\"},{\"count\":\"2\",\"date\":\"2023-04-23\"},{\"count\":\"3\",\"date\":\"2023-04-24\"},{\"count\":\"2\",\"date\":\"2023-04-25\"},{\"count\":\"3\",\"date\":\"2023-04-26\"},{\"count\":\"4\",\"date\":\"2023-04-27\"},{\"count\":\"1\",\"date\":\"2023-04-28\"},{\"count\":\"3\",\"date\":\"2023-04-29\"},{\"count\":\"2\",\"date\":\"2023-05-01\"},{\"count\":\"3\",\"date\":\"2023-05-03\"},{\"count\":\"4\",\"date\":\"2023-05-04\"},{\"count\":\"3\",\"date\":\"2023-05-05\"},{\"count\":\"2\",\"date\":\"2023-05-06\"},{\"count\":\"1\",\"date\":\"2023-05-08\"},{\"count\":\"3\",\"date\":\"2023-05-09\"},{\"count\":\"10\",\"date\":\"2023-05-10\"},{\"count\":\"3\",\"date\":\"2023-05-11\"},{\"count\":\"4\",\"date\":\"2023-05-12\"},{\"count\":\"2\",\"date\":\"2023-05-13\"},{\"count\":\"1\",\"date\":\"2023-05-14\"},{\"count\":\"2\",\"date\":\"2023-05-16\"},{\"count\":\"3\",\"date\":\"2023-05-17\"},{\"count\":\"4\",\"date\":\"2023-05-18\"},{\"count\":\"2\",\"date\":\"2023-05-19\"},{\"count\":\"1\",\"date\":\"2023-05-20\"},{\"count\":\"2\",\"date\":\"2023-05-21\"}],\"events_count_by_month\":[{\"count\":\"20\",\"date\":\"2023-04\"},{\"count\":\"75\",\"date\":\"2023-05\"},{\"count\":\"41\",\"date\":\"2023-06\"},{\"count\":\"39\",\"date\":\"2023-07\"},{\"count\":\"39\",\"date\":\"2023-08\"},{\"count\":\"70\",\"date\":\"2023-09\"},{\"count\":\"32\",\"date\":\"2023-10\"},{\"count\":\"64\",\"date\":\"2023-11\"},{\"count\":\"124\",\"date\":\"2023-12\"},{\"count\":\"115\",\"date\":\"2024-01\"},{\"count\":\"115\",\"date\":\"2024-02\"},{\"count\":\"110\",\"date\":\"2024-03\"},{\"count\":\"160\",\"date\":\"2024-04\"},{\"count\":\"136\",\"date\":\"2024-05\"},{\"count\":\"141\",\"date\":\"2024-06\"},{\"count\":\"100\",\"date\":\"2024-07\"},{\"count\":\"104\",\"date\":\"2024-08\"},{\"count\":\"105\",\"date\":\"2024-09\"},{\"count\":\"155\",\"date\":\"2024-10\"},{\"count\":\"82\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"504\",\"date\":\"2023\"},{\"count\":\"1323\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"},{\"events\":[{\"author\":null,\"occurred_at\":\"2023-04-26T01:10:35.000Z\",\"url\":\"https:\\/\\/doi.org\\/10.48550\\/arxiv.2005.13012\"}],\"events_count\":1,\"events_count_by_day\":[{\"count\":1,\"date\":\"2023-04-26\"}],\"events_count_by_month\":[{\"count\":1,\"date\":\"2023-04\"}],\"events_count_by_year\":[{\"count\":1,\"date\":\"2023\"}],\"first_event_date\":\"2023-04-26\",\"source\":{\"display_name\":\"DataCite\",\"icon_url\":\"https:\\/\\/api.paperbuzz.org\\/static\\/img\\/favicons\\/datacite.gif\",\"id\":\"datacite\"},\"source_id\":\"datacite\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewjcce3202838\",\"doi\":\"10.47852\\/bonviewjcce3202838\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewjcce3202838\",\"ISSN\":[\"2810-9503\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewjcce3202838\",\"abstract\":\"<jats:p>The BERT model has arisen as a popular state-of-the-art model in recent years. 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Comparing BERT Against Traditional Machine Learning Models in Text Classification | Journal of Computational and Cognitive Engineering