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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Frutiger 45 Light",sans-serif;color:#1F497D">*<b>This email is being forwarded to you at the request of Dr. Razvan Bunescu</b>*<o:p></o:p></span></p>
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<p><span style="font-family:"Calibri",sans-serif;color:black">=======================================================<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><b><u><span style="font-family:"Calibri",sans-serif;color:black">CS 6840: Natural Language Processing</span></u></b><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p style="margin-bottom:12.0pt"><b><span style="font-family:"Calibri",sans-serif;color:black">Time and Location:</span></b><span style="font-family:"Calibri",sans-serif;color:black"> Tue, Thu 9:00 – 10:20am, ARC 159 </span><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><b><span style="font-family:"Calibri",sans-serif;color:black">Instructor:</span></b><span style="font-family:"Calibri",sans-serif;color:black"> Razvan Bunescu <o:p></o:p></span></p>
<p><b><span style="font-family:"Calibri",sans-serif;color:black">Course description:</span></b><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><span style="font-family:"Calibri",sans-serif;color:black">Natural Language Processing (NLP) is a branch of Artificial Intelligence concerned with developing computer systems that can process or generate natural language. Major applications of NLP include
machine translation, sentiment analysis, speech recognition, information retrieval / web search engines, question answering, or information extraction. In this course, students will learn how to use modern machine learning (ML) techniques to solve fundamental
NLP tasks, such as training vector-based representations of words and their meanings, document classification, syntactic parsing, language modeling, coreference resolution, entity linking, and semantic parsing. </span><span style="font-family:"Arial",sans-serif;color:black"><br>
<br>
</span><b><span style="font-family:"Calibri",sans-serif;color:black">Prerequisites:</span></b><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><span style="font-family:"Calibri",sans-serif;color:black">Students are expected to be comfortable with programming in Python and have basic knowledge of formal languages (regular and context free grammars), linear algebra, probability theory and statistics.
Knowledge of deep learning will be very useful, though not strictly necessary as long as the student is willing to learn. Each ML-based model will be introduced in class and relevant supplemental online materials will be provided throughout the course.<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black">Links to online reading materials and other details are available on the course web page at:<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><a href="http://ace.cs.ohio.edu/~razvan/courses/nlp6840/index.html" id="LPlnk329426">http://ace.cs.ohio.edu/~razvan/courses/nlp6840/index.html</a><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Frutiger 45 Light",sans-serif;color:#1F497D">*<b>This email is being forwarded to you at the request of Dr. Razvan Bunescu</b>*<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Frutiger 45 Light",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<div id="divtagdefaultwrapper">
<p><span style="font-family:"Calibri",sans-serif;color:black">=======================================================<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><b><u><span style="font-family:"Calibri",sans-serif;color:black">CS 6840: Natural Language Processing</span></u></b><span style="font-family:"Calibri",sans-serif;color:black"><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p style="margin-bottom:12.0pt"><b><span style="font-family:"Calibri",sans-serif;color:black">Time and Location:</span></b><span style="font-family:"Calibri",sans-serif;color:black"> Tue, Thu 9:00 – 10:20am, ARC 159 </span><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><b><span style="font-family:"Calibri",sans-serif;color:black">Instructor:</span></b><span style="font-family:"Calibri",sans-serif;color:black"> Razvan Bunescu <o:p></o:p></span></p>
<p><b><span style="font-family:"Calibri",sans-serif;color:black">Course description:</span></b><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><span style="font-family:"Calibri",sans-serif;color:black">Natural Language Processing (NLP) is a branch of Artificial Intelligence concerned with developing computer systems that can process or generate natural language. Major applications of NLP include
machine translation, sentiment analysis, speech recognition, information retrieval / web search engines, question answering, or information extraction. In this course, students will learn how to use modern machine learning (ML) techniques to solve fundamental
NLP tasks, such as training vector-based representations of words and their meanings, document classification, syntactic parsing, language modeling, coreference resolution, entity linking, and semantic parsing. </span><span style="font-family:"Arial",sans-serif;color:black"><br>
<br>
</span><b><span style="font-family:"Calibri",sans-serif;color:black">Prerequisites:</span></b><span style="font-family:"Arial",sans-serif;color:black"><br>
</span><span style="font-family:"Calibri",sans-serif;color:black">Students are expected to be comfortable with programming in Python and have basic knowledge of formal languages (regular and context free grammars), linear algebra, probability theory and statistics.
Knowledge of deep learning will be very useful, though not strictly necessary as long as the student is willing to learn. Each ML-based model will be introduced in class and relevant supplemental online materials will be provided throughout the course.<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black">Links to online reading materials and other details are available on the course web page at:<o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><a href="http://ace.cs.ohio.edu/~razvan/courses/nlp6840/index.html" id="LPlnk329426">http://ace.cs.ohio.edu/~razvan/courses/nlp6840/index.html</a><o:p></o:p></span></p>
<p><span style="font-family:"Calibri",sans-serif;color:black"><o:p> </o:p></span></p>
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