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 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">MOSCOW ECONOMIC JOURNAL</journal-id>
   <journal-title-group>
    <journal-title xml:lang="en">MOSCOW ECONOMIC JOURNAL</journal-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Московский экономический журнал</trans-title>
    </trans-title-group>
   </journal-title-group>
   <issn publication-format="online">2413-046X</issn>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="publisher-id">74489</article-id>
   <article-categories>
    <subj-group subj-group-type="toc-heading" xml:lang="ru">
     <subject>Отраслевая и региональная экономика</subject>
    </subj-group>
    <subj-group subj-group-type="toc-heading" xml:lang="en">
     <subject></subject>
    </subj-group>
    <subj-group>
     <subject>Отраслевая и региональная экономика</subject>
    </subj-group>
   </article-categories>
   <title-group>
    <article-title xml:lang="en">КОНТЕНТ-АНАЛИЗ WEB-ДОКУМЕНТОВ СОГЛАСНО ПОИСКОВЫМ ЗАПРОСАМ</article-title>
    <trans-title-group xml:lang="ru">
     <trans-title>КОНТЕНТ-АНАЛИЗ WEB-ДОКУМЕНТОВ СОГЛАСНО ПОИСКОВЫМ ЗАПРОСАМ</trans-title>
    </trans-title-group>
   </title-group>
   <contrib-group content-type="authors">
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Плотников</surname>
       <given-names>Андрей Викторович</given-names>
      </name>
      <name xml:lang="en">
       <surname>Plotnikov</surname>
       <given-names>Andrey Viktorovich</given-names>
      </name>
     </name-alternatives>
     <email>andreiplotnikovwork@gmail.com</email>
     <xref ref-type="aff" rid="aff-1"/>
    </contrib>
   </contrib-group>
   <aff-alternatives id="aff-1">
    <aff>
     <institution xml:lang="ru">Пермский государственный аграрно-технологический университет имени академика Д.Н. Прянишникова</institution>
     <country>ru</country>
    </aff>
    <aff>
     <institution xml:lang="en">Пермский государственный аграрно-технологический университет имени академика Д.Н. Прянишникова</institution>
     <country>ru</country>
    </aff>
   </aff-alternatives>
   <pub-date publication-format="print" date-type="pub" iso-8601-date="2017-12-25T14:01:20+03:00">
    <day>25</day>
    <month>12</month>
    <year>2017</year>
   </pub-date>
   <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2017-12-25T14:01:20+03:00">
    <day>25</day>
    <month>12</month>
    <year>2017</year>
   </pub-date>
   <volume>2</volume>
   <issue>5</issue>
   <fpage>4</fpage>
   <lpage>4</lpage>
   <history>
    <date date-type="received" iso-8601-date="2017-12-07T14:01:20+03:00">
     <day>07</day>
     <month>12</month>
     <year>2017</year>
    </date>
    <date date-type="accepted" iso-8601-date="2017-12-15T14:01:20+03:00">
     <day>15</day>
     <month>12</month>
     <year>2017</year>
    </date>
   </history>
   <self-uri xlink:href="https://qje.su/en/nauka/article/74489/view">https://qje.su/en/nauka/article/74489/view</self-uri>
   <abstract xml:lang="ru">
    <p>В работе рассматривается метод обработки информации на естественном языке латентно-семантический анализ, анализирующий взаимосвязь между коллекцией документов и словами в них встречающимися. Анализ пользовательского запроса был произведен в системах Яндекс и Google. Рассмотрены показатели term frequency - показатель частоты или плотности вхождений термина в конкретном документе, а также показатель соотношения количества использования термина и суммарного числа слов в документе и inverse document frequency - обратная частота документа относительно запроса, то есть отношение всей подборки документов в поисковой базе к тем, что содержат в себе заданный термин.</p>
   </abstract>
   <trans-abstract xml:lang="en">
    <p>The paper considers the method of processing information in natural language latent-semantic analysis, which analyzes the relationship between the collection of documents and the words they meet. The analysis of the user request was made in Yandex and Google systems. The term frequency parameters are considered - an indicator of the frequency or density of occurrences of a term in a specific document, as well as an indicator of the ratio between the use of the term and the total number of words in the document and the inverse document frequency - the inverse frequency of the document with respect to the query, that is the ratio of the entire collection of documents in the search database to those, that contain a given term.</p>
   </trans-abstract>
   <kwd-group xml:lang="ru">
    <kwd>латентно-семантический анализ</kwd>
    <kwd>латентно-семантический индекс</kwd>
    <kwd>поисковая оптимизация</kwd>
    <kwd>поисковый маркетинг</kwd>
    <kwd>контент-маркетинг</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>latent-semantic analysis</kwd>
    <kwd>latent-semantic index</kwd>
    <kwd>search engine optimization</kwd>
    <kwd>search engine marketing</kwd>
    <kwd>content marketing</kwd>
   </kwd-group>
  </article-meta>
 </front>
 <body>
  <p>The paper considers the method of processing information in natural language latent-semantic analysis, which analyzes the relationship between the collection of documents and the words they meet. The analysis of the user request was made in Yandex and Google systems. The term frequency parameters are considered - an indicator of the frequency or density of occurrences of a term in a specific document, as well as an indicator of the ratio between the use of the term and the total number of words in the document and the inverse document frequency - the inverse frequency of the document with respect to the query, that is the ratio of the entire collection of documents in the search database to those, that contain a given term.</p>
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