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<!--
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=human+language">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=human+language]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for human language]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/482/Kelvin-Information-Extraction-System"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/261/WIkipedia-as-an-ontology"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/482/Kelvin-Information-Extraction-System">
  <title><![CDATA[Kelvin Information Extraction System]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/482/Kelvin-Information-Extraction-System</link>
  <description><![CDATA[I'll describe recent work on the Kelvin information extraction system and its performance in two tasks in the 2015 NIST Text Analysis Conference.  Kelvin has been under development at the JHU Human Language Center of Excellence for several years.  Kelvin reads documents in several languages and extracts entities and relations between them. This year it was used for the Coldstart Knowledge Base Population and Trilingual Entity Discovery and Linking tasks.  Key components in the tasks are a sys...]]></description>
  <dc:date>2015-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing">
  <title><![CDATA[A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/323/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-via-Linguistic-Analysis-Entropy-based-Label-Ranking-and-Semantic-Typing</link>
  <description><![CDATA[Zareen Syed will talk about "A Hybrid Approach to Unsupervised Relation Discovery via Linguistic Analysis, Entropy-based Label Ranking and Semantic Typing" 

ABSTRACT:
There are today two main approaches in Information Extraction systems to extract entities and relations between them from text: a knowledge engineering approach which requires grammars to be hand crafted to express the rules for the system, a quite laborious process; an automatic training approach which requires the hand ann...]]></description>
  <dc:date>2009-10-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach">
  <title><![CDATA[Real-Time Detection of Online Health Misinformation using an Integrated Knowledgegraph-LLM Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach</link>
  <description><![CDATA[Winner of Best Student Paper Award 
The dramatic surge of health misinformation on social media platforms poses a significant threat to public health, contributing to hesitancy in vaccines, delayed medical interventions, and the adoption of untested or harmful treatments. We present a novel, hybrid AI-driven framework designed for the real-time detection of health misinformation on social media platforms while prioritizing user privacy. The framework integrates the strengths of Large Langua...]]></description>
  <dc:date>2025-07-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution">
  <title><![CDATA[Using Wikitology for Cross-Document Entity Coreference Resolution]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution</link>
  <description><![CDATA[We describe the use of the Wikitology knowledge base as a resource for a variety of applications, with special focus on a cross-document entity coreference resolution task. This task involves recognizing when entities and relations mentioned in different documents refer to the same object or relation in the world. Wikitology is a knowledge base system constructed with material from Wikipedia, DBpedia, and Freebase that includes both unstructured text and semi-structured information. Wikitolog...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery">
  <title><![CDATA[Knowledge Base Evaluation for Semantic Knowledge Discovery]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery</link>
  <description><![CDATA[Semantic knowledge discovery has traditionally been evaluated at the text level. For example, evaluations such as MUC and ACE evaluate the information extraction of particular types of semantic roles and relations primarily at the mention level. We suggest that evaluating at the level of a knowledge base (KB) extracted from the text has significant advantages over evaluation at the text level. By knowledge base, we mean the combination of a database, a descriptive schema for the contents of t...]]></description>
  <dc:date>2008-11-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure">
  <title><![CDATA[JETS: Achieving Completeness through Coverage and Closure]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure</link>
  <description><![CDATA[Work in progress on JETS, the successor to PLANES, is described. JETS is a natural language question answering system intended to interface users with a large relational database. The architecture is designed to extend the conceptual coverage of JETS to better meet the conversational and database usage requirements of users. The implementation of JETS is designed to gain a high degree of closure over concept manipulation, contributing to a solution to the problems of perspicuity and scale. Sp...]]></description>
  <dc:date>1979-08-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/261/WIkipedia-as-an-ontology">
  <title><![CDATA[WIkipedia as an ontology]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/261/WIkipedia-as-an-ontology</link>
  <description><![CDATA[There is a lot of 'semantic information' on the Web, the vast
majority of which is encoded as human language text. This is
especially true for content found on the social web, consisting
of blogs, Wikis, forums, and many other social media systems. One
way to accelerate the realization of the Semantic Web's vision of
a web of machine understandable data is to extract semantic
information from this text and publish it in structured or
semi-structured forms (e.g., RDF) using appropriate ...]]></description>
  <dc:date>2009-03-24</dc:date>
 </item>
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