<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462022000301225</article-id>
<article-id pub-id-type="doi">10.13053/cys-26-3-4343</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A Study on Stochastic Variational Inference for Topic Modeling with Word Embeddings]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ozaki]]></surname>
<given-names><![CDATA[Kana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Kobayashie]]></surname>
<given-names><![CDATA[Ichiro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Ochanomizu University  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Japan</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>26</volume>
<numero>3</numero>
<fpage>1225</fpage>
<lpage>1232</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462022000301225&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462022000301225&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462022000301225&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Probabilistic topic models based on Latent Dirichlet Allocation (LDA) is widely used to extract latent topics from document collections. In recent years, a number of extended topic models have been proposed, especially Gaussian LDA (G-LDA) has attracted a lot of attention. G-LDA integrates topic modeling with word embeddings by replacing discrete topic distributions over words with multivariate Gaussian distributions on the word embedding space. This can reflect semantic information into topics. In this paper, we use G-LDA for our base topic model and apply Stochastic Variational Inference (SVI), an efficient inference algorithm, to estimate topics. Through experiments, we could extract the topics with high coherence in practical time.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Topic model]]></kwd>
<kwd lng="en"><![CDATA[latent Dirichlet allocation]]></kwd>
<kwd lng="en"><![CDATA[word embeddings]]></kwd>
<kwd lng="en"><![CDATA[stochastic variational inference]]></kwd>
</kwd-group>
</article-meta>
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