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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=lda">
  <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=lda]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for lda]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/8/DAML-ITTalks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/762/ALDA-Cognitive-Assistant-for-Legal-Document-Analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering"/>
    </rdf:Seq>
  </items>
 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of
text documents and have become an important technique for systems that
measure document similarity for classification, clustering,
segmentation, entity linking and more.  While they have been applied
to some non-text domains, their use for semi-structured graph data,
such as RDF, has been less explored.  We present a framework for
applying topic modeling to RDF graph data and describe how it can be
used in a number o...]]></description>
  <dc:date>2015-09-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics">
  <title><![CDATA[ALDA: Automated Legal Document Analytics]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/105/ALDA-Automated-Legal-Document-Analytics</link>
  <description><![CDATA[There has been an exponential growth in use of digitized legal documents in recent years. Majority of services on the Internet have associated legal documents such as Terms of Services, Privacy Policies and Service Level agreements. A large corpus of court cases, judgments and compliance/regulations are now digitally available for e-discovery. Moreover, businesses are maintaining large data sets of legal contracts that they have signed with their employees, customers and contractors. Furtherm...]]></description>
  <dc:date>2014-06-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/8/DAML-ITTalks">
  <title><![CDATA[DAML / ITTalks]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/8/DAML-ITTalks</link>
  <description><![CDATA[DAML Tools for supporting Intelligent Information Annotation, Sharing and RetrievalWith the vast quantity of information now available on the Internet, there is a need to manage this information by marking it with a semantic language, such as DARPA Agent Markup Language (DAML), and using intelligent search engines and other tools, in conjunction with ontology-based matching, to provide better search results and data manipulation capabilities. The aim of the semantic web is to make the current...]]></description>
  <dc:date>2000-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration">
  <title><![CDATA[Two Tier Analysis of Social Media Collaboration for Student Migration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration</link>
  <description><![CDATA[Global adoption of Social Media as the preferred medium for collaboration and information exchange is increasingly reshaping social realities and facilitating new research methodologies in various disciplines. Social Media applications are collecting a large amount of User-Generated Content (UGC) and web data that contains knowledge about novel approaches of global collaboration between people. We have done a detailed study of the factors that lead to student migration, as espoused by social ...]]></description>
  <dc:date>2019-12-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents">
  <title><![CDATA[Understanding the Logical and Semantic Structure of Large Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents</link>
  <description><![CDATA[Current language understanding approaches are mostly focused on small documents, such as newswire articles, blog posts, and product reviews. Understanding and extracting information from large documents like legal documents, reports, proposals, technical manuals, and research articles is still a challenging task. Because the documents may be multi-themed, complex, and cover diverse topics. The content can be split into multiple files or aggregated into one large file. As a result, the content...]]></description>
  <dc:date>2018-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/762/ALDA-Cognitive-Assistant-for-Legal-Document-Analytics">
  <title><![CDATA[ALDA : Cognitive Assistant for Legal Document Analytics]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/762/ALDA-Cognitive-Assistant-for-Legal-Document-Analytics</link>
  <description><![CDATA[In recent times, there has been an exponential growth in digitization of legal documents such as case records, contracts,
terms of services, regulations, privacy documents and compliance guidelines. Courts have been digitizing their archived
cases and also making it available for e-discovery. On the
other hand, businesses are now maintaining large data sets
of legal contracts that they have signed with their employees,
customers and contractors. Large public sector organizations
are oft...]]></description>
  <dc:date>2016-09-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking, and more.  While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored.  We present a framework for applying topic modeling to RDF graph data and describe how it can be used in a number of link...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering">
  <title><![CDATA[Entity Disambiguation for Wild Big Data Using Multi-Level Clustering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/717/Entity-Disambiguation-for-Wild-Big-Data-Using-Multi-Level-Clustering</link>
  <description><![CDATA[When RDF instances represent the same entity they are said
to corefer. For example, two nodes from different RDF graphs 1 both refer
to same individual, musical artist James Brown. Disambiguating entities
is essential for knowledge base population and other tasks that result
in integration or linking of data. Often however, entity instance data
originates from different sources and can be represented using differ-
ent schemas or ontologies. In the age of Big Data, data can have other
c...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
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