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<h1 class="page-header">Class-based Prediction Errors to Categorize Text with Out-of-vocabulary Words</h1>
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<span property="dc:title" content="Class-based Prediction Errors to Categorize Text with Out-of-vocabulary Words" class="rdf-meta element-hidden"></span><span property="sioc:num_replies" content="0" datatype="xsd:integer" class="rdf-meta element-hidden"></span> </header>
<div id="biblio-node"><span class="Z3988" title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Class-based+Prediction+Errors+to+Categorize+Text+with+Out-of-vocabulary+Words&rft.series=ALW1%26%23039%3B17&rft.date=2017&rft.aulast=Serr%C3%A0&rft.aufirst=Joan&rft.au=Leontiadis%2C+Ilias&rft.au=Spathis%2C+Dimitris&rft.au=Stringhini%2C+Gianluca&rft.au=Blackburn%2C+Jeremy&rft.au=Vakali%2C+Athena&rft.place=Vancouver%2C+Canada"></span><div class="table-responsive">
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<tr><td class="biblio-row-title">Title</td><td>Class-based Prediction Errors to Categorize Text with Out-of-vocabulary Words</td> </tr>
<tr><td class="biblio-row-title">Publication Type</td><td>Conference Proceedings</td> </tr>
<tr><td class="biblio-row-title">Year of Conference</td><td>2017</td> </tr>
<tr><td class="biblio-row-title">Authors</td><td><a href="publications%3Ff[author]=401.html" rel="nofollow">Serrà, Joan</a>, <a href="publications%3Ff[author]=402.html" rel="nofollow">Ilias Leontiadis</a>, <a href="publications%3Ff[author]=403.html" rel="nofollow">Dimitris Spathis</a>, <a href="publications%3Ff[author]=396.html" rel="nofollow">Gianluca Stringhini</a>, <a href="publications%3Ff[author]=394.html" rel="nofollow">Jeremy Blackburn</a>, and <a href="publications%3Ff[author]=5.html" rel="nofollow">Athena Vakali</a></td> </tr>
<tr><td class="biblio-row-title">Series Title</td><td>ALW1'17</td> </tr>
<tr><td class="biblio-row-title">Conference Location</td><td>Vancouver, Canada</td> </tr>
<tr><td class="biblio-row-title">Abstract</td><td><p>Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or non-blacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%.</p>
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