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  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers">
    <rdfs:label><![CDATA[Information Extraction via Automatic Generation of Semantic Classifiers]]></rdfs:label>
    <event:title><![CDATA[Information Extraction via Automatic Generation of Semantic Classifiers]]></event:title>
    <event:speaker><person:PhDAlumnus rdf:about="http://ebiquity.umbc.edu/person/html/Zareen/Syed/"><person:name><![CDATA[Zareen  Syed]]></person:name><rdfs:label><![CDATA[Zareen  Syed]]></rdfs:label></person:PhDAlumnus></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2008-09-16T10:30:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2008-09-16T12:00:00-05:00</event:endDate>
    <event:location><![CDATA[ITE 346]]></event:location>
    <event:abstract><![CDATA[Information extraction is an important unsolved problem of natural
language processing (NLP). It is the problem of extracting entities
(such as people, organizations or locations) and named relations
between entities (such as "People born-in Country") from text
documents. An important challenge in information extraction is the
labeling of training data which is usually done manually and is
therefore very expensive.<br/>
<p>
This talk introduces a new "model" to generate training data with
least manual intervention. Our approach uses structured data available
in Encarta (Encyclopedia) to generate the training data. Encarta
articles are categorized and linked to related articles by experts. We
harvest the structured data available in Encarta and use it in an
intuitive way  for automatic generation of classifiers. The
classifiers were employed on the following information extraction
tasks:

<ul>
<li> Entity Classification</li>
<li>  Entity Clustering</li>
<li> Relation Extraction</li>
</ul>

We also tested our classifiers automatically built from Encarta on
Wikipedia articles. In addition to that we conducted experiments to
evaluate the performance of features extracted using MindNet, a
lexical knowledge base that can be constructed fully automatically
from text, built from Encarta.
<p>
The talk will also cover the challenges faced in using the Encarta and
MindNet resources and give an overview of promising future work
directions.]]></event:abstract>
    <event:uri><![CDATA[http://ebiquity.umbc.edu/]]></event:uri>
    <event:tag><![CDATA[information extraction]]></event:tag>
    <event:tag><![CDATA[natural language processing]]></event:tag>
    <event:tag><![CDATA[encarta]]></event:tag>
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