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<oembed><version>1.0</version><provider_name>Department of Computer Science</provider_name><provider_url>https://www.cs.jhu.edu</provider_url><title>Computer Science Student Defense: Dingquan Wang, Johns Hopkins University &#x2013; &#x201C;Supervised Training on Synthetic Languages: A Novel Framework for Unsupervised Parsing&#x201D; - Department of Computer Science</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content"&gt;&lt;a href="https://www.cs.jhu.edu/event/computer-science-student-defense-dingquan-wang-johns-hopkins-university-supervised-training-on-synthetic-languages-a-novel-framework-for-unsupervised-parsing/"&gt;Computer Science Student Defense: Dingquan Wang, Johns Hopkins University &#x2013; &#x201C;Supervised Training on Synthetic Languages: A Novel Framework for Unsupervised Parsing&#x201D;&lt;/a&gt;&lt;/blockquote&gt;
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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/computer-science-student-defense-dingquan-wang-johns-hopkins-university-supervised-training-on-synthetic-languages-a-novel-framework-for-unsupervised-parsing/embed/" width="600" height="338" title="&#x201C;Computer Science Student Defense: Dingquan Wang, Johns Hopkins University &#x2013; &#x201C;Supervised Training on Synthetic Languages: A Novel Framework for Unsupervised Parsing&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationMalone 107AbstractThis talk focuses on unsupervised dependency parsing&#x2014;parsing sentences of a language into dependency trees without accessing the training data of that language. Different from most prior work that uses unsupervised learning to estimate the parsing parameters, we estimate the parameters by supervised training on synthetic languages. Our parsing framework has three major components: Synthetic&hellip;</description></oembed>
