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            <title xml:lang="en">Automatic Extraction of IDM-Related Information in Scientific Articles and Online Science News Websites</title>
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                <forename type="first">Oriane</forename>
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            <idno type="halRefHtml">&lt;i&gt;18th TRIZ Future Conference (TFC)&lt;/i&gt;, Oct 2018, Strasbourg, France. pp.213-224, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-02456-7_18"&gt;&amp;#x27E8;10.1007/978-3-030-02456-7_18&amp;#x27E9;&lt;/a&gt;</idno>
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                  <date type="end">2018-10-31</date>
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                <editor>Denis Cavallucci</editor>
                <editor>Roland De Guio</editor>
                <editor>Sebastian Koziołek</editor>
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                <term xml:lang="en">Knowledge extraction</term>
                <term xml:lang="en">Machine learning</term>
                <term xml:lang="en">Inventive Design</term>
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              <p>Previous studies have made it possible to extract information related to IDM (Inventive Design Method) out of patents. IDM is an ontology-defined method derived from TRIZ. As its mother theory, IDM is primarily based on patent’s observation and aims at finding inventive solutions on the basis of contradictions. In this paper, we present a new approach for extracting knowledge, this time out of other types of science-related documents: scientific papers and science news articles. This approach is based on sets of linguistics features which have been selected and evaluated semi-automatically with techniques of Natural Language Processing as well as Machine Learning.</p>
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