<?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>S1405-55462017000400759</article-id>
<article-id pub-id-type="doi">10.13053/cys-21-4-2847</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Hybrid Attention Networks for Chinese Short Text Classification]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Yujun]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Jiaming]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[Jie]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Bo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Changliang]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Bo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Institute of Automation  ]]></institution>
<addr-line><![CDATA[Beijing ]]></addr-line>
<country>P.R. China</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,University of Chinese Academy of Sciences  ]]></institution>
<addr-line><![CDATA[Beijing ]]></addr-line>
<country>P.R. China</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Jiangsu Jinling Science and Technology Group Co., Ltd  ]]></institution>
<addr-line><![CDATA[Nanjing ]]></addr-line>
<country>P.R. China</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2017</year>
</pub-date>
<volume>21</volume>
<numero>4</numero>
<fpage>759</fpage>
<lpage>769</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462017000400759&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462017000400759&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462017000400759&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: To improve the classification performance for Chinese short text with automatic semantic feature selection, in this paper we propose the Hybrid Attention Networks (HANs) which combines the word- and character-level selective attentions. The model firstly applies RNN and CNN to extract the semantic features of texts. Then it captures class-related attentive representation from word- and character-level features. Finally, all of the features are concatenated and fed into the output layer for classification. Experimental results on 32-class and 5-class datasets show that, our model outperforms multiple baselines by combining not only the word- and character-level features of the texts, but also class-related semantic features by attentive mechanism.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Chinese short texts]]></kwd>
<kwd lng="en"><![CDATA[text classification]]></kwd>
<kwd lng="en"><![CDATA[attentive mechanism]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural network]]></kwd>
<kwd lng="en"><![CDATA[recurrent neural network]]></kwd>
</kwd-group>
</article-meta>
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<conf-name><![CDATA[ IEEE/WIC/ACM International Conference on Web Intelligence]]></conf-name>
<conf-date>2016</conf-date>
<conf-loc> </conf-loc>
<page-range>137&#8211;144</page-range></nlm-citation>
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</back>
</article>
