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  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Artificial Intelligence, Applications and Innovations</JournalTitle>
      <Issn>3060-7124</Issn>
      <Volume>1</Volume>
      <Issue>Journal of Artificial Intelligence, Application and Inovations</Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>07</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>A Deductive Word Sense Disambiguation Approach Based on Data Mining and Knowledge Extraction in Expert Systems</ArticleTitle>
    <VernacularTitle>A Deductive Word Sense Disambiguation Approach Based on Data Mining and Knowledge Extraction in Expert Systems</VernacularTitle>
    <FirstPage>20</FirstPage>
    <LastPage>30</LastPage>
    <ELocationID EIdType="doi">10.61838/jaiai.1.3.3</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2024</Year>
        <Month>03</Month>
        <Day>05</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;Word Sense Disambiguation (WSD) involves assigning the appropriate sense to ambiguous words. WSD is one of the most challenging problems in several Natural Language Processing (NLP) tasks, such as machine translation. This paper proposes a novel approach consisting of four main components. In the first part, a mining process is used to construct a tree structure that represents helpful knowledge about the conceptual relationships between each ambiguous word and its relevant context. In the second part, a Knowledge Base (KB) is constructed based on the chains derived from the tree structure. The third part involves designing an expert system for lexical ambiguity resolution using the forward chaining strategy. In the final part, the KB is upgraded to improve its effectiveness in determining the correct senses of ambiguous words. The performance of the proposed approach is evaluated on the TWA corpus. The results demonstrate the effectiveness of the proposed expert system.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Natural language processing</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Expert system</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Word sense disambiguation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Lexical ambiguity resolution</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Forward chaining</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalaiai.com/index.php/aiai/article/download/32/16</ArchiveCopySource>
  </Article>
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