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<Paper uid="C02-1151">
  <Title>Probabilistic Reasoning for Entity &amp; Relation Recognition/</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper develops a method for recognizing relations and entities in sentences, while taking mutual dependencies among them into account. E.g., the kill (Johns, Oswald) relation in: &amp;quot;J. V. Oswald was murdered at JFK after his assassin, K. F. Johns...&amp;quot; depends on identifying Oswald and Johns as people, JFK being identified as a location, and the kill relation between Oswald and Johns; this, in turn, enforces that Oswald and Johns are people.</Paragraph>
    <Paragraph position="1"> In our framework, classifiers that identify entities and relations among them are first learned from local information in the sentence; this information, along with constraints induced among entity types and relations, is used to perform global inference that accounts for the mutual dependencies among the entities.</Paragraph>
    <Paragraph position="2"> Our preliminary experimental results are promising and show that our global inference approach improves over learning relations and entities separately.</Paragraph>
  </Section>
class="xml-element"></Paper>