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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=coreference">
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      <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/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/873/Extracting-Rich-Semantic-Information-about-Cybersecurity-Events"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/908/Modeling-and-Extracting-Information-about-Cybersecurity-Events-from-Text"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/671/KELVIN-Extracting-Knowledge-from-Large-Text-Collections"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data"/>
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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/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching">
  <title><![CDATA[Cluster-based Instance Consolidation For Subsequent Matching]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/439/Cluster-based-Instance-Consolidation-For-Subsequent-Matching</link>
  <description><![CDATA[In this week's lab meeting Jennifer Sleaman will present "Cluster-based Instance Consolidation For Subsequent Matching"

Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich...]]></description>
  <dc:date>2012-10-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/873/Extracting-Rich-Semantic-Information-about-Cybersecurity-Events">
  <title><![CDATA[Extracting Rich Semantic Information about Cybersecurity Events]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/873/Extracting-Rich-Semantic-Information-about-Cybersecurity-Events</link>
  <description><![CDATA[Articles about cybersecurity events like data breaches and ransomware attacks are common, both in general news and technical sources.  Automatically extracting structured information from these can provide valuable information to inform both human analysts and computer systems. In this paper we describe how cybersecurity events can be described via semantic schemas, examined through an initial set of five event types. Using a collection of 1,000 news articles annotated with these event types,...]]></description>
  <dc:date>2019-12-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/908/Modeling-and-Extracting-Information-about-Cybersecurity-Events-from-Text">
  <title><![CDATA[Modeling and Extracting Information about Cybersecurity Events from Text]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/908/Modeling-and-Extracting-Information-about-Cybersecurity-Events-from-Text</link>
  <description><![CDATA[People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with many problems, including cybercrimes, stolen data, and identity theft. With the large and increasing number of transactions done every day, the frequency of cybercrime events is also growing. Since the number of security-related events is too high for manual review and monitoring, we...]]></description>
  <dc:date>2019-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/729/Event-Nugget-Detection-using-Thresholding-and-Classification-Techniques">
  <title><![CDATA[Event Nugget Detection using Thresholding and Classification Techniques]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/729/Event-Nugget-Detection-using-Thresholding-and-Classification-Techniques</link>
  <description><![CDATA[This paper describes our Event Nugget Detection system that we submitted to the TAC KBP 2015. We considered the problem as document classification problem. We used confidence scores from classifier to detect the event by thresholding. Our feature vectors consist of event nugget context, part of speech tags of event nugget context and semantic similarity score between event nugget and event subtypes.  Our performance was low; we got F1 measure of 0.33 for both event nugget detection task and c...]]></description>
  <dc:date>2015-11-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Entity Type Recognition for Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/665/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered.  Big Data problems that involve integrating data from multiple sources can benefit...]]></description>
  <dc:date>2015-03-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/671/KELVIN-Extracting-Knowledge-from-Large-Text-Collections">
  <title><![CDATA[KELVIN: Extracting Knowledge from Large Text Collections]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/671/KELVIN-Extracting-Knowledge-from-Large-Text-Collections</link>
  <description><![CDATA[We describe the KELVIN system for extracting entities and relations from large text collections and its use in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and Technology.  The Cold Start task starts with an empty knowledge base defined by an ontology or entity types, properties, and relations.  Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to de-emphasize the linking entities to a background know...]]></description>
  <dc:date>2014-11-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/637/HLTCOE-Participation-at-TAC-2013">
  <title><![CDATA[HLTCOE Participation at TAC 2013]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/637/HLTCOE-Participation-at-TAC-2013</link>
  <description><![CDATA[The JHU HLTCOE participated in the Entity Linking and Cold Start Knowledge Base tasks in this year’s Text Analysis Conference Knowledge Base Population evaluation.  We have previously participated in TAC-KBP evaluations in 2009, 2010, 2011, and 2012. Our primary focus this year was on the Cold Start task; improvements to our existing KELVIN system included consolidating slot values for an entity, removal of suspect intra-document conference chains, streamlined cross-document entity corefere...]]></description>
  <dc:date>2013-11-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Entity Type Recognition for Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/636/Entity-Type-Recognition-for-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach to reducing the computational cost of identifying coreferential instances in heterogeneous semantic graphs, where the underlying ontologies may be uninformative or even unknown. The problem is similar to coreference resolution in unstructured text, where a variety of linguistic clues and contextual information is used to infer entity types and predict coreference. Semantic graphs, whether in RDF or another formalism, are semi-structured data with varied contextual clue...]]></description>
  <dc:date>2013-11-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs">
  <title><![CDATA[Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/631/Type-Prediction-for-Efficient-Coreference-Resolution-in-Heterogeneous-Semantic-Graphs</link>
  <description><![CDATA[We describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreference resolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are uninformative or unknown. This problem is similar to coreference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Se...]]></description>
  <dc:date>2013-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data">
  <title><![CDATA[Online unsupervised coreference resolution for semi-structured heterogeneous data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/633/Online-unsupervised-coreference-resolution-for-semi-structured-heterogeneous-data</link>
  <description><![CDATA[A pair of RDF instances are said to corefer when they are intended to denote the same
thing in the world, for example, when two nodes of type foaf:Person describe the same
individual. This problem is central to integrating and inter-linking semi-structured
datasets. We are developing an online, unsupervised coreference resolution framework
for heterogeneous, semi-structured data. The online aspect requires us to process
new instances as they appear and not as a batch. The instances are h...]]></description>
  <dc:date>2012-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/603/Cluster-based-Instance-Consolidation-For-Subsequent-Matching">
  <title><![CDATA[Cluster-based Instance Consolidation For Subsequent Matching]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/603/Cluster-based-Instance-Consolidation-For-Subsequent-Matching</link>
  <description><![CDATA[Instance consolidation is a way to merge instances that are thought to be the same or closely related that can be used to support coreference resolution and entity linking. For Semantic Web data, consolidating instances can be as simple as relating instances using owl:sameAs, as is the case in linked data, or merging instances that could then be used to populate or enrich a knowledge model. In many applications, systems process data incrementally over time and as new data is processed, the st...]]></description>
  <dc:date>2012-11-12</dc:date>
 </item>
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