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  <title><![CDATA[Joint Models to Refine Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/910/Joint-Models-to-Refine-Knowledge-Graphs</link>
  <description><![CDATA[A knowledge graph can be viewed as a structural representation of beliefs with nodes and edges in which the nodes represent real-world entities or events and the edges are relations believed to hold between pairs of entities. Multiple levels of processes are involved in extracting such knowledge graphs from natural language text, starting with reading and understanding the text, then constructing a graph of the entities found and the relations between them, and inferring missing relations tha...]]></description>
  <dc:date>2019-12-01</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs">
  <title><![CDATA[Joint Models to Refine Knowledge Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/871/Joint-Models-to-Refine-Knowledge-Graphs</link>
  <description><![CDATA[A knowledge graph can be viewed as a structural representation of beliefs with nodes and edges in which the nodes represent real-world entities or events and the edges are relations believed to hold between pairs of entities. Multiple levels of processes are involved in extracting such knowledge graphs from natural language text, starting with reading and understanding the text, then constructing a graph of the entities found and the relations between them, and inferring missing relations tha...]]></description>
  <dc:date>2019-10-25</dc:date>
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  <title><![CDATA[SURFACE: Semantically Rich Fact Validation with Explanations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/842/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations</link>
  <description><![CDATA[Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant eviden...]]></description>
  <dc:date>2018-11-01</dc:date>
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  <title><![CDATA[SURFACE: Semantically Rich Fact Validation with Explanations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/866/SURFACE-Semantically-Rich-Fact-Validation-with-Explanations</link>
  <description><![CDATA[Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify input sentences as either SUPPORTED, REFUTED or NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task and explains its decision through a selection of sentences from the trusted source. Our multi-task neural approach uses semantic lexical frames from FrameNet to jointly (i) find relevant eviden...]]></description>
  <dc:date>2018-10-31</dc:date>
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  <title><![CDATA[A Unified Bayesian Model of Scripts, Frames and Language.]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/819/A-Unified-Bayesian-Model-of-Scripts-Frames-and-Language-</link>
  <description><![CDATA[We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fill-more's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fill-more's frames, as originally laid out by those authors. Empirically, our approa...]]></description>
  <dc:date>2016-02-12</dc:date>
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