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            <title xml:lang="en">Novel Techniques for Text Annotation with Wikipedia Entities</title>
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            <idno type="halRefHtml">&lt;i&gt;10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)&lt;/i&gt;, Sep 2014, Rhodes, Greece. pp.508-518, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-662-44654-6_50"&gt;&amp;#x27E8;10.1007/978-3-662-44654-6_50&amp;#x27E9;&lt;/a&gt;</idno>
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            <note type="commentary">Part 12: Data Mining-Forecasting</note>
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                <title xml:lang="en">Novel Techniques for Text Annotation with Wikipedia Entities</title>
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                  <date type="end">2014-09-21</date>
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                <editor>Lazaros Iliadis</editor>
                <editor>Ilias Maglogiannis</editor>
                <editor>Harris Papadopoulos</editor>
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                <term xml:lang="en">Semantic Data Linking on Web</term>
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              <p>Text annotation is the procedure of identifying the semantically dominant words of a text segment and attaching them with conceptual content information in their context. In this paper, we propose novel methods for automatic annotation of text fragments with entities of Wikipedia, the largest knowledge base online, a process commonly known as Wikification aiming at resolving the semantics of synonymous and polysemous terms accurately. The cornerstone of our contribution is a novel iterative Wikification approach, converging at optimal annotations while balancing high accuracy with performance. Our first two methods can be fine-tuned through a machine-learning technique over large homogenous data sets. Our experimental evaluation resulted in remarkable improvement over state-of-the-art Wikification approaches.</p>
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